IN2012DE00401A - - Google Patents

Download PDF

Info

Publication number
IN2012DE00401A
IN2012DE00401A IN401DE2012A IN2012DE00401A IN 2012DE00401 A IN2012DE00401 A IN 2012DE00401A IN 401DE2012 A IN401DE2012 A IN 401DE2012A IN 2012DE00401 A IN2012DE00401 A IN 2012DE00401A
Authority
IN
India
Prior art keywords
mixture model
subsets
subset
dataset
general
Prior art date
Application number
Inventor
Robert Edward Callan
Brian Larder
Original Assignee
Gen Electric
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Gen Electric filed Critical Gen Electric
Publication of IN2012DE00401A publication Critical patent/IN2012DE00401A/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases

Abstract

A method of constructing a general mixture model (100) of a dataset includes partitioning the dataset into at least two subsets (104) according to predefined criteria (108), generating a subset mixture model for each of the at least two subsets (110), and then combining the mixture models from each subset to generate a general mixture model (120).
IN401DE2012 2011-02-15 2012-02-13 IN2012DE00401A (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US13/027,829 US20120209880A1 (en) 2011-02-15 2011-02-15 Method of constructing a mixture model

Publications (1)

Publication Number Publication Date
IN2012DE00401A true IN2012DE00401A (en) 2015-06-05

Family

ID=45655746

Family Applications (1)

Application Number Title Priority Date Filing Date
IN401DE2012 IN2012DE00401A (en) 2011-02-15 2012-02-13

Country Status (7)

Country Link
US (1) US20120209880A1 (en)
EP (1) EP2490139B1 (en)
JP (1) JP6001871B2 (en)
CN (1) CN102693265B (en)
BR (1) BR102012003344A2 (en)
CA (1) CA2767504A1 (en)
IN (1) IN2012DE00401A (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9251203B2 (en) * 2012-12-22 2016-02-02 Mmodal Ip Llc User interface for predictive model generation
CA2932069A1 (en) 2013-11-29 2015-06-04 Ge Aviation Systems Limited Method of construction of anomaly models from abnormal data
CN106156857B (en) * 2015-03-31 2019-06-28 日本电气株式会社 The method and apparatus of the data initialization of variation reasoning
CN106156077A (en) * 2015-03-31 2016-11-23 日本电气株式会社 The method and apparatus selected for mixed model
US10817796B2 (en) * 2016-03-07 2020-10-27 D-Wave Systems Inc. Systems and methods for machine learning
CN107644279A (en) * 2016-07-21 2018-01-30 阿里巴巴集团控股有限公司 The modeling method and device of evaluation model
CN109559214A (en) 2017-09-27 2019-04-02 阿里巴巴集团控股有限公司 Virtual resource allocation, model foundation, data predication method and device
CN109657802B (en) * 2019-01-28 2020-12-29 清华大学深圳研究生院 Hybrid expert reinforcement learning method and system
CN112990337B (en) * 2021-03-31 2022-11-29 电子科技大学中山学院 Multi-stage training method for target identification

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6449612B1 (en) * 1998-03-17 2002-09-10 Microsoft Corporation Varying cluster number in a scalable clustering system for use with large databases
US6263337B1 (en) * 1998-03-17 2001-07-17 Microsoft Corporation Scalable system for expectation maximization clustering of large databases
US7039239B2 (en) * 2002-02-07 2006-05-02 Eastman Kodak Company Method for image region classification using unsupervised and supervised learning
US7299135B2 (en) * 2005-11-10 2007-11-20 Idexx Laboratories, Inc. Methods for identifying discrete populations (e.g., clusters) of data within a flow cytometer multi-dimensional data set
US7664718B2 (en) * 2006-05-16 2010-02-16 Sony Corporation Method and system for seed based clustering of categorical data using hierarchies
US8432449B2 (en) * 2007-08-13 2013-04-30 Fuji Xerox Co., Ltd. Hidden markov model for camera handoff
JP2009086581A (en) * 2007-10-03 2009-04-23 Toshiba Corp Apparatus and program for creating speaker model of speech recognition
US8521659B2 (en) * 2008-08-14 2013-08-27 The United States Of America, As Represented By The Secretary Of The Navy Systems and methods of discovering mixtures of models within data and probabilistic classification of data according to the model mixture
US8493409B2 (en) * 2009-08-18 2013-07-23 Behavioral Recognition Systems, Inc. Visualizing and updating sequences and segments in a video surveillance system
CN101882150B (en) * 2010-06-09 2012-09-26 南京大学 Three-dimensional model comparison and search method based on nuclear density estimation
US8571328B2 (en) * 2010-08-16 2013-10-29 Adobe Systems Incorporated Determining correspondence between image regions

Also Published As

Publication number Publication date
CN102693265B (en) 2017-08-25
EP2490139B1 (en) 2020-04-01
CN102693265A (en) 2012-09-26
JP2012168949A (en) 2012-09-06
US20120209880A1 (en) 2012-08-16
BR102012003344A2 (en) 2015-08-04
CA2767504A1 (en) 2012-08-15
EP2490139A1 (en) 2012-08-22
JP6001871B2 (en) 2016-10-05

Similar Documents

Publication Publication Date Title
IN2012DE00401A (en)
GB201108200D0 (en) User input prediction
EA201490172A1 (en) SYSTEM AND METHOD FOR FORMING A GEOSTATISTICAL MODEL OF AN INTERESTING GEOLOGICAL VOLUME LIMITED TO A PROCESS ORIENTED MODEL OF AN INTERESTING GEOLOGICAL VOLUME
GB201314204D0 (en) Three-dimensional moedeling parameters for oilfield drilling
MX2013011973A (en) Grid from depositional space.
WO2013016679A3 (en) Systems and methods for generating and using a digital pass
GB2493413B (en) Maintaining and supplying speech models
GB201116221D0 (en) Autonomous vehicle and task modelling
MX344125B (en) Modifying structured search queries on online social networks.
MY167697A (en) Method for producing 4,4-difluoro-3,4- dihydroisoquinoline derivatives
GB201217271D0 (en) Traffic sensor management
EA201491306A1 (en) METHOD FOR PRODUCING HYDROCARBONS USING A CAVERN
GB2489638A (en) System and method for performing an adaptive drilling operation
GB2503391A (en) Graph databases for storing multidimensional models of software offerings
MX2015005627A (en) Systems and methods for 3d seismic data depth conversion utilizing artificial neural networks.
WO2014022317A3 (en) Multi-level reservoir history matching
GB2542053A (en) Automatically generating a semantic mapping for a relational database
MY192410A (en) Velocity model update with an inversion gradient
MX371169B (en) Predicting multiples in survey data.
WO2014058575A3 (en) Modeling data generating process
WO2013081655A3 (en) Method and system for generating and/or repairing a surface model of a geometric structure
IN2014DN08134A (en)
GB201218266D0 (en) Representing geological objects specified through time in a spatial geology modeling framework
WO2014088917A8 (en) Music creation systems and methods
IN2014CH01330A (en)