KR20230148912A - Method, server and computer program for providing machine-learning based final decision support system - Google Patents

Abstract

A method for providing a machine learning-based final decision-making system according to various embodiments of the present invention for realizing the above-described tasks is disclosed. The method includes collecting a plurality of data, generating analysis information corresponding to the plurality of data using a decision model, and generating at least one decision information based on the analysis information. It may be characterized in that the decision-making model includes a plurality of decision-making sub-models that perform different steps.

Description

Method, server, and computer program for providing a machine learning-based final decision-making system {METHOD, SERVER AND COMPUTER PROGRAM FOR PROVIDING MACHINE-LEARNING BASED FINAL DECISION SUPPORT SYSTEM}

The present invention relates to a decision support system, and more specifically, to a method, server, and computer program for providing a decision support system based on machine learning.

Manual, report-based decision-making support not only difficult to ensure the reliability of results, but also had the limitation of being a one-time solution and unable to respond efficiently to rapidly changing environmental conditions.

In order to overcome these limitations, the reliability of prediction results can be guaranteed by linking IT technology and decision support technology to provide objective and standardized results, and a decision support system can efficiently respond to rapidly changing environmental conditions. Support system development is actively taking place.

A decision support system is a system that processes and analyzes large amounts of data to extract the knowledge necessary for decision making and provide it to users. For example, the knowledge necessary for decision-making can be extracted by utilizing various types of structured and unstructured data, such as data and documents on product sales and the knowledge of organization members.

However, the conventional decision-making system had a problem in that it did nothing more than analyze data so that users could easily make decisions.

Korean Patent Publication No. 10-2005-0011590 (January 29, 2005)

The problem to be solved by the present invention was developed in response to the above-mentioned background technology, and can provide a decision support system based on machine learning.

A method for providing a machine learning-based final decision-making system according to an embodiment of the present invention to solve the above-mentioned problems is disclosed. The method includes collecting a plurality of data, generating analysis information corresponding to the plurality of data using a decision model, and generating at least one decision information based on the analysis information. It may be characterized in that the decision-making model includes a plurality of decision-making sub-models that perform different steps.

In an alternative embodiment, the plurality of decision-making sub-models include: a first decision-making sub-model generating first analysis information corresponding to the data data, and a second decision-making sub-model generating second analysis information corresponding to the data data. It may include a decision-making sub-model, a third decision-making sub-model generating third analysis information corresponding to the data, and a fourth decision-making sub-model generating fourth analysis information corresponding to the data.

In an alternative embodiment, the first decision sub-model is characterized in that it generates the analysis information corresponding to the material data through a plurality of steps, wherein the plurality of steps include a first step related to information search. , may include a second stage related to comparison of existing information, a third stage related to in-depth information review, and a fourth stage related to advanced information decision.

According to another embodiment of the present invention, a server for performing a method for providing a machine learning-based final decision-making system is disclosed. The server includes a memory that stores one or more instructions and a processor that executes one or more instructions stored in the memory, and the processor executes the one or more instructions to provide the machine learning-based final decision-making system described above. method can be performed.

According to another embodiment of the present invention, a computer program stored in a computer-readable recording medium is disclosed. The computer program can be combined with a hardware computer to perform the method for providing the machine learning-based final decision-making system described above.

Other specific details of the invention are included in the detailed description and drawings.

According to various embodiments of the present invention, it is possible to provide final decision-making information based on analysis related to data.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

Figure 1 is an example diagram schematically showing a system for implementing a method for providing a machine learning-based final decision-making system according to an embodiment of the present invention.
Figure 2 is a hardware configuration diagram of a server that performs a method for providing a machine learning-based final decision-making system according to an embodiment of the present invention.
Figure 3 is a flowchart illustrating a method for providing a machine learning-based final decision-making system related to an embodiment of the present invention.
Figure 4 is a flowchart illustrating a plurality of steps performed through a first decision sub-model related to an embodiment of the present invention.
Figure 5 is an exemplary diagram illustrating a method of operating a plurality of decision-making sub-models included in a decision-making model related to an embodiment of the present invention.
Figure 6 is a diagram illustrating a process for deriving output through mutual connection between decision-making sub-models related to an embodiment of the present invention.
Figure 7 is a schematic diagram showing one or more network functions related to the present invention.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the invention. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as departing from the scope of the present invention.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the invention. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In this specification, a computer refers to all types of hardware devices including at least one processor, and depending on the embodiment, it may be understood as encompassing software configurations that operate on the hardware device. For example, a computer can be understood to include, but is not limited to, a smartphone, tablet PC, desktop, laptop, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and depending on the embodiment, at least part of each step may be performed in a different device.

Figure 1 is an example diagram schematically showing a system for implementing a method for providing a machine learning-based final decision-making system according to an embodiment of the present invention.

As shown in FIG. 1, a system according to embodiments of the present invention may include a server 100, a user terminal 200, an external server 300, and a network 400. The components shown in FIG. 1 are exemplary, and additional components may exist or some of the components shown in FIG. 1 may be omitted. The server 100, the external server 300, and the user terminal 200 according to embodiments of the present invention can mutually transmit and receive data for the system according to embodiments of the present invention through the network 400. there is.

The network 400 according to embodiments of the present invention includes Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and Very Various wired communication systems such as High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN) can be used.

In addition, the network 400 presented herein includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and Single Carrier-FDMA (SC-FDMA). A variety of wireless communication systems may be used, such as FDMA) and other systems.

The network 400 according to embodiments of the present invention can be configured regardless of the communication mode, such as wired or wireless, and can be configured in various communication modes such as a personal area network (PAN) and a wide area network (WAN). It may consist of a communication network. In addition, the network 400 may be the known World Wide Web (WWW), and may use wireless transmission technology used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth. . The techniques described herein can be used in the networks mentioned above, as well as other networks.

According to an embodiment of the present invention, the server 100 (hereinafter referred to as 'server 100') that provides a machine learning-based final decision-making system can generate decision-making information based on a plurality of data. The plurality of data is information that is the basis for generating decision-making information and may include, for example, business-related data, investment-related data, purchase-related data, etc. The specific description of the plurality of data is only an example, and the present invention is not limited thereto.

The server 100 may generate analysis information corresponding to a plurality of data through a learned neural network model, and may generate at least one decision-making information based on the analysis information. The neural network model learned here may be a neural network model.

In other words, the server 100 can generate and provide final decision-making information, that is, decision-making information related to corporate decision-making, user purchase-related decision-making, etc., through analysis of various data. A specific method by which the server 100 generates and provides decision-making information will be described later with reference to FIG. 3.

According to one embodiment of the present invention, the server 100 may be a server that provides cloud computing services. More specifically, the server 100 may be a type of Internet-based computing server that provides a cloud computing service that processes information not on the user's computer but on another computer connected to the Internet. The cloud computing service may be a service that stores data on the Internet and allows users to use it anytime, anywhere through Internet access without having to install necessary data or programs on their computer. The cloud computing service can be used to easily manipulate and manipulate data stored on the Internet. You can easily share and forward with a click. In addition, cloud computing services not only allow you to simply store data on a server on the Internet, but also allow you to perform desired tasks using the functions of applications provided on the web without having to install a separate program, and allow multiple people to view documents at the same time. It may be a service that allows you to work while sharing. Additionally, cloud computing services may be implemented in at least one of the following forms: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), virtual machine-based cloud server, and container-based cloud server. . That is, the server 100 of the present invention may be implemented in at least one form among the cloud computing services described above. The specific description of the cloud computing service described above is merely an example, and may include any platform for constructing the cloud computing environment of the present invention.

The user terminal 200 according to an embodiment of the present invention may refer to any type of node(s) in a system that has a mechanism for communication with the server 100. For example, this user terminal 200 may include a PC, laptop computer, workstation, terminal, and/or any electronic device having network 400 connectivity. Additionally, the user terminal may include an arbitrary server implemented by at least one of an agent, an application programming interface (API), and a plug-in. Additionally, the user terminal 200 may include an application source and/or a user terminal application.

In one embodiment, the user terminal 200 may be connected to the server 100 through the network 400, may provide a plurality of data to the server 100, and may respond to the plurality of data provided. Decision-making information can be provided.

In one embodiment, the external server 300 may be connected to the server 100 through the network 400, and various information necessary for the server 100 to perform a method for providing a final decision-making system based on machine learning. /Data can be provided or result data derived from performing a method to provide a machine learning-based final decision-making system can be provided, stored, and managed. For example, the external server 300 may be a storage server separately provided outside the server 100, but is not limited thereto. Hereinafter, with reference to FIG. 2, the hardware configuration of the server 100 that performs a method for providing a machine learning-based final decision-making system will be described.

Figure 2 is a hardware configuration diagram of a server that performs a method for providing a machine learning-based final decision-making system according to an embodiment of the present invention.

Referring to FIG. 2, the server 100 that provides a machine learning-based final decision-making system according to another embodiment of the present invention includes one or more processors 110 and a computer program 151 performed by the processors 110. It may include a memory 120 to load, a bus 130, a communication interface 140, and a storage 150 to store a computer program 151. Here, only components related to the embodiment of the present invention are shown in Figure 2. Accordingly, anyone skilled in the art to which the present invention pertains will know that other general-purpose components may be included in addition to the components shown in FIG. 2.

According to one embodiment of the present invention, the processor 110 can typically process the overall operation of the server 100. The processor 110 provides or processes appropriate information or functions to the user or user terminal by processing signals, data, information, etc. input or output through the components discussed above or by running an application program stored in the memory 120. can do.

Additionally, the processor 110 may perform operations on at least one application or program for executing methods according to embodiments of the present invention, and the server 100 may include one or more processors.

According to one embodiment of the present invention, the processor 110 may be composed of one or more cores, such as a central processing unit (CPU) of a computing device, and a general purpose graphics processing unit (GPGPU). , may include a processor for data analysis and deep learning, such as a tensor processing unit (TPU).

The processor 110 may read a computer program stored in the memory 120 and perform data processing for deep learning according to an embodiment of the present invention. According to one embodiment of the present invention, the processor 110 may perform calculations for learning a neural network. The processor 110 is used for learning neural networks, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. Calculations can be performed.

Additionally, the processor 110 allows at least one of the CPU, GPGPU, and TPU to process learning of the network function. For example, CPU and GPGPU can work together to process learning of network functions and data classification using network functions. Additionally, in one embodiment of the present invention, the processors of a plurality of computing devices can be used together to process learning of network functions and data classification using network functions. Additionally, a computer program executed in a computing device according to an embodiment of the present invention may be a CPU, GPGPU, or TPU executable program.

In this specification, network function may be used interchangeably with artificial neural network or neuron network. In this specification, a network function may include one or more neural networks, and in this case, the output of the network function may be an ensemble of the outputs of one or more neural networks.

The processor 110 may read the computer program stored in the memory 120 and provide a method for providing a final decision-making system based on machine learning according to an embodiment of the present invention.

According to one embodiment of the present invention, the processor 110 may generate decision-making information through analysis of a plurality of data. According to an embodiment of the present invention, the processor 110 may perform calculations to learn a decision-making model.

In various embodiments, the processor 110 includes random access memory (RAM) (not shown) and read memory (ROM) that temporarily and/or permanently store signals (or data) processed within the processor 110. -Only Memory, not shown) may be further included. Additionally, the processor 110 may be implemented in the form of a system on chip (SoC) that includes at least one of a graphics processing unit, RAM, and ROM.

Memory 120 stores various data, commands and/or information. Memory 120 may load a computer program 151 from storage 150 to execute methods/operations according to various embodiments of the present invention. When the computer program 151 is loaded into the memory 120, the processor 110 can perform the method/operation by executing one or more instructions constituting the computer program 151. The memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

The bus 130 provides communication functions between components of the server 100. The bus 130 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The communication interface 140 supports wired and wireless Internet communication of the server 100. Additionally, the communication interface 140 may support various communication methods other than Internet communication. To this end, the communication interface 140 may be configured to include a communication module well known in the technical field of the present invention. In some embodiments, communication interface 140 may be omitted.

Storage 150 may store the computer program 151 non-temporarily. When performing a process for providing a machine learning-based final decision-making system through the server 100, the storage 150 can store various information necessary to provide a process for providing a machine learning-based final decision-making system. there is.

The storage 150 is a non-volatile memory such as Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, a hard disk, a removable disk, or a device well known in the technical field to which the present invention pertains. It may be configured to include any known type of computer-readable recording medium.

The computer program 151, when loaded into the memory 120, may include one or more instructions that cause the processor 110 to perform methods/operations according to various embodiments of the present invention. That is, the processor 110 can perform the method/operation according to various embodiments of the present invention by executing the one or more instructions.

In one embodiment, the computer program 151 includes collecting a plurality of data, generating analysis information corresponding to the plurality of data using a decision-making model, and generating at least one analysis information based on the analysis information. It may include one or more instructions for performing a method for providing a machine learning-based final decision-making system including the step of generating decision-making information.

The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. The software module may be RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) and stored in a medium in order to be executed in conjunction with a hardware computer. Components of the invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as combinations of data structures, processes, routines or other programming constructs, such as C, C++, , may be implemented in a programming or scripting language such as Java, assembler, etc. Functional aspects may be implemented as algorithms running on one or more processors. Hereinafter, with reference to FIGS. 3 to 7, a method for providing a final decision-making system based on machine learning performed by the server 100 will be described in detail later.

Figure 3 is a flow chart illustrating a method for providing a machine learning-based final decision-making system related to an embodiment of the present invention. The order of the steps shown in FIG. 3 may be changed as needed, and at least one step may be omitted or added. That is, the following steps are only an example of the present invention, and the scope of the present invention is not limited thereto.

According to an embodiment of the present invention, the server 100 can collect a plurality of data (S110). The plurality of data is information that is the basis for generating decision-making information and may include, for example, business-related data, investment-related data, purchase-related data, etc. The specific description of the plurality of data is only an example, and the present invention is not limited thereto.

Specifically, the plurality of data may include at least one of an institution's infrastructure database, non-computerized data, and externally disclosed data. For example, if a machine learning-based final decision-making system supports decision-making necessary for school operation, multiple pieces of data may be related to on- and off-campus data. In other words, the basic database may be uDRIMS, entrance exam DRIMS, Dream PATH+, e-Class, etc., non-computerized data may be analysis data such as data (Excel), and external public data may be university information disclosure data. The above-described plurality of data are merely examples to aid understanding of the present invention, and the present invention is not limited thereto. In various embodiments, the plurality of data may further include data referenced for decision-making related to a purchase or data referenced for decision-making related to a company.

In an embodiment, the server 100 may collect data distributed on the web or SNS using an ETL (Extraction, Transformation, Loading) method. For example, acquiring data by extracting it from one or more data sources, or transforming it in the form of data cleansing, format conversion, standardization, integration, or applying business rules embedded in multiple applications. A loading process can be performed to load data into a specific target system.

Additionally, in an embodiment, the server 100 may collect logs from a specific computer system, collect information obtained from mail, or collect a plurality of data from data sources by using methods such as search or crawling. .

According to an embodiment of the present invention, the server 100 may generate analysis information corresponding to a plurality of data using a decision-making model (S120). In an embodiment, the decision-making model may be configured to include a plurality of decision-making sub-models that perform different steps. Specifically, the plurality of decision-making submodels include a first decision-making submodel that generates first analysis information corresponding to data data, a second decision-making submodel that generates second analysis information corresponding to data data, and data. It may include a third decision-making sub-model that generates third analysis information corresponding to data and a fourth decision-making sub-model that generates fourth analysis information corresponding to material data.

That is, the decision-making model may be composed of decision-making sub-models composed of different stages, and each decision-making sub-model may derive different analysis information in response to the same plurality of data.

In more detail, the first decision-making sub-model may be characterized by generating analysis information corresponding to data through a plurality of steps. The steps performed by the first decision-making sub-model may be as shown in FIG. 4.

Referring to FIG. 4, the first decision sub-model may perform the first step related to information search (S210). In an embodiment, in the first step, the 37% rule related to the optimal stopping problem may be applied. For example, the best stopping problem may be related to the definition that the optimal expected value can be obtained with 37% effort.

Additionally, the first decision sub-model may perform the second step related to comparing existing information (S220). According to the embodiment, the second step may be a step related to exploration and exploitation trade-off. For example, there may be a trade-off where searching to gather new information takes a long time, and utilization can be used quickly, but there is no improvement. In an embodiment, in the second step, at least one of exploration and utilization may be determined through user responses to additional queries. Following this process, you can improve your next selection. In the case of utilization, selection (or decisions) related to decision making can be made through comparison of existing data (eg, experienced data).

Additionally, the first decision-making sub-model can perform the third step related to in-depth information psychology (S230). In the third step, the least recently used principle can be applied. The most recent and least used principle is to eliminate decisions that need to be removed based on 'how long have I had it', 'is it still available', 'does it overlap with existing information' and 'when was the last time I used it'. will be. This could mean focusing on the decisions that have been used the least recently.

Additionally, the first decision sub-model can perform the fourth step related to advanced information decision (S240). The fourth step may be a step of performing comparison between the results of different decision-making submodels. High-level information decisions can be made based on linkages between decision-making submodels.

For example, referring to Figure 5, the first decision sub-model performs steps 1 to 4, the second decision sub-model performs steps 2 to 4, and the third decision sub-model performs steps 2 to 4. The decision sub-model performs the fourth step of the third step, and the fourth decision sub-model may perform the fourth step. In other words, each decision-making sub-model can be applied differentially to output analysis information related to decision-making. In other words, as shown in FIG. 6, the server 100 may implement a decision-making model that generates optimal decision-making information by connecting decision-making sub-models that perform different steps in a specific loop.

According to one embodiment of the present invention, the server 100 may generate at least one decision information based on the analysis information (S130). In other words, the server 100 can generate and provide final decision-making information, that is, decision-making information related to corporate decision-making, user purchase-related decision-making, etc., through analysis of various data.

Figure 7 is a schematic diagram showing one or more network functions related to one embodiment of the present invention.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network can generally consist of a set of interconnected computational units, which can be referred to as “nodes”. These “nodes” may also be referred to as “neurons.” A neural network consists of at least one node. The nodes (or neurons) that make up neural networks may be interconnected by one or more “links.”

Within a neural network, one or more nodes connected through a link may form a relative input node and output node relationship. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, the nodes connecting the input node and the output node may have a weight. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network can be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if there are two neural networks with the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of one or more nodes. Some of the nodes constituting the neural network may form one layer based on the distances from the first input node. For example, a set of nodes with a distance n from the first input node is, n layers can be configured. The distance from the initial input node can be defined by the minimum number of links that must be passed to reach the node from the initial input node. However, this definition of a layer is arbitrary for explanation purposes, and the order of a layer within a neural network may be defined in a different way than described above. For example, a layer of nodes may be defined by distance from the final output node.

The initial input node may refer to one or more nodes in the neural network through which data is directly input without going through links in relationships with other nodes. Alternatively, in the relationship between nodes based on a link within a neural network, it may refer to nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in their relationship with other nodes among the nodes in the neural network. Additionally, hidden nodes may refer to nodes constituting a neural network other than the first input node and the last output node. The neural network according to an embodiment of the present invention is a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as it progresses from the input layer to the hidden layer. You can. In addition, the neural network according to another embodiment of the present invention may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present invention is a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. You can. The neural network according to another embodiment of the present invention may be a neural network that is a combination of the above-described neural networks.

A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to an input layer and an output layer. Deep neural networks allow you to identify latent structures in data. In other words, it is possible to identify the potential structure of a photo, text, video, voice, or music (e.g., what object is in the photo, what the content and emotion of the text are, what the content and emotion of the voice are, etc.) . Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBM). machine), deep belief network (DBN), Q network, U network, Siamese network, etc. The description of the deep neural network described above is only an example and the present invention is not limited thereto.

A neural network can be trained in at least one of supervised learning, unsupervised learning, and semi-supervised learning. Learning of a neural network is intended to minimize errors in output. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (i.e., labeled learning data), and in the case of non-teacher learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of teacher learning regarding data classification, the learning data may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network and the label of the training data. As another example, in the case of non-teachable learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the reverse direction (i.e., from the output layer to the input layer) in the neural network, and the connection weight of each node in each layer of the neural network can be updated according to backpropagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network training, a high learning rate can be used to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and in the later stages of training, a low learning rate can be used to increase accuracy.

In the learning of neural networks, the training data can generally be a subset of real data (i.e., the data to be processed using the learned neural network), and thus the error for the training data is reduced, but the error for the real data is reduced. There may be an incremental learning cycle. Overfitting is a phenomenon in which errors in actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that learned a cat by showing a yellow cat fails to recognize that it is a cat when it sees a non-yellow cat may be a type of overfitting. Overfitting can cause errors in machine learning algorithms to increase. To prevent such overfitting, various optimization methods can be used. To prevent overfitting, methods such as increasing the learning data, regularization, or dropout, which omits some of the network nodes during the learning process, can be applied.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. (Hereinafter, it is described collectively as a neural network.) The data structure may include a neural network. And the data structure including the neural network may be stored in a computer-readable medium. Data structures including neural networks may also include data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and loss functions for learning the neural network. there is. A data structure containing a neural network may include any of the components disclosed above. In other words, the data structure including the neural network includes all or all of the data input to the neural network, the weights of the neural network, the hyperparameters of the neural network, the data acquired from the neural network, the activation function associated with each node or layer of the neural network, and the loss function for training the neural network. It may be configured to include any combination of. In addition to the configurations described above, a data structure containing a neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include all types of data used or generated in the computational process of a neural network and is not limited to the above. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node.

The data structure may include data input to the neural network. A data structure containing data input to a neural network may be stored in a computer-readable medium. Data input to the neural network may include learning data input during the neural network learning process and/or input data input to the neural network on which training has been completed. Data input to the neural network may include data that has undergone pre-processing and/or data subject to pre-processing. Preprocessing may include a data processing process to input data into a neural network. Therefore, the data structure may include data subject to preprocessing and data generated by preprocessing. The above-described data structure is only an example and the present invention is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used with the same meaning.) And the data structure including the weights of the neural network may be stored in a computer-readable medium. A neural network may include multiple weights. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the parameters. The above-described data structure is only an example and the present invention is not limited thereto.

As an example and not a limitation, the weights may include weights that are changed during the neural network learning process and/or weights for which neural network learning has been completed. Weights that change during the neural network learning process may include weights that change at the start of the learning cycle and/or weights that change during the learning cycle. Weights for which neural network training has been completed may include weights for which a learning cycle has been completed. Therefore, the data structure including the weights of the neural network may include weights that are changed during the neural network learning process and/or the data structure including the weights for which neural network learning has been completed. Therefore, the above-mentioned weights and/or combinations of each weight are included in the data structure including the weights of the neural network. The above-described data structure is only an example and the present invention is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or a different computing device and later reorganized and used. Computing devices can transmit and receive data over a network by serializing data structures. Data structures containing the weights of a serialized neural network can be reconstructed on the same computing device or on a different computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure to increase computational efficiency while minimizing the use of computing device resources (e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree) may be included. The foregoing is only an example and the present invention is not limited thereto.

The data structure may include hyper-parameters of a neural network. And the data structure including the hyperparameters of the neural network can be stored in a computer-readable medium. A hyperparameter may be a variable that can be changed by the user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle repetitions, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit. It may include a number (e.g., number of hidden layers, number of nodes in hidden layers). The above-described data structure is only an example and the present invention is not limited thereto.

The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. The software module may be RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) and stored in a medium in order to be executed in conjunction with a hardware computer. Components of the invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as combinations of data structures, processes, routines or other programming constructs, such as C, C++, , may be implemented in a programming or scripting language such as Java, assembler, etc. Functional aspects may be implemented as algorithms running on one or more processors.

Those skilled in the art will understand that various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein can be used in electronic hardware, (for convenience) It will be understood that the implementation may be implemented by various forms of program or design code (referred to herein as “software”) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the present invention.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory. Includes, but is not limited to, devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information. The term “machine-readable media” includes, but is not limited to, wireless channels and various other media capable of storing, retaining, and/or transmitting instruction(s) and/or data.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present invention, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the invention. Thus, the present invention is not limited to the embodiments presented herein, but is to be construed in the broadest scope consistent with the principles and novel features presented herein.

Claims (5)

A method performed on one or more processors of a computing device, comprising:
collecting a plurality of data;
generating analysis information corresponding to the plurality of data using a decision-making model; and
generating at least one decision information based on the analysis information;
Includes,
The decision-making model is,
Characterized by comprising a plurality of decision-making sub-models that perform different steps from each other,
A method for providing a machine learning-based final decision-making system.
According to paragraph 1,
The plurality of decision-making submodels are:
a first decision-making sub-model that generates first analysis information corresponding to the data;
a second decision-making sub-model that generates second analysis information corresponding to the data;
a third decision-making sub-model that generates third analysis information corresponding to the material data; and
a fourth decision-making sub-model that generates fourth analysis information corresponding to the data;
Including,
A method for providing a machine learning-based final decision-making system.
According to paragraph 2,
The first decision sub-model is,
Characterized by generating the analysis information corresponding to the data data through a plurality of steps,
The plurality of steps are,
The first stage involves information seeking;
The second stage involves comparing existing information;
Stage 3, which involves in-depth information hearing; and
Step 4, which involves making high-level information decisions;
Including,
A method for providing a machine learning-based final decision-making system. A memory that stores one or more instructions; and
a processor executing one or more instructions stored in the memory; Including,
The processor executes the one or more instructions,
A computing device that performs the method of claim 1.
A computer program combined with a computer as hardware and stored in a computer-readable recording medium so as to perform the method of claim 1.