JP2013058095A5

JP2013058095A5 -

Info

Publication number: JP2013058095A5
Application number: JP2011196300A
Authority: JP
Filing date: 2011-09-08
Publication date: 2014-09-25
Anticipated expiration: 2031-09-08

Description

（オンライン学習の効果について）
上記の言語解析機の自動構築方法を用いて実験を行った。その実験結果を図３９に示す。図３９に示したグラフの横軸は経過時間（単位は日）であり、縦軸は平均Ｆ値（ＡｖｅｒａｇｅＦ−Ｍｅａｓｕｒｅｓ）である。また、実線（Ｏｎｌｉｎｅ，１ｋ）及び破線（Ｏｎｌｉｎｅ，４ｋ）は、オンライン学習による学習用データセットの逐次更新を続けた場合の実験結果を示している。一方、鎖線（Ｏｆｆｌｉｎｅ，１ｋ）及び一点鎖線（Ｏｆｆｌｉｎｅ，４ｋ）は、オフライン学習による実験結果を示している。なお、１ｋは、推定機の構築に利用する学習用データの数を１０００個に設定したことを示している。一方、４ｋは、推定機の構築に利用する学習用データの数を４０００個に設定したことを示している。 (About the effects of online learning)
Experiments were conducted using the above-mentioned automatic construction method for language analyzers. The experimental results are shown in FIG. The horizontal axis of the graph shown in FIG. 39 is the elapsed time (the unit is days), and the vertical axis is the average F value (Average F-Measures). In addition, a solid line (Online, 1k) and a broken line (Online, 4k) indicate experimental results when the learning data set is continuously updated by online learning. On the other hand, a chain line ( Offline , 1k) and an alternate long and short dash line ( Offline , 4k) indicate the experimental results by offline learning. Note that 1k indicates that the number of learning data used to construct the estimator is set to 1000. On the other hand, 4k indicates that the number of learning data used to construct the estimator is set to 4000.

Claims

When a plurality of learning data composed of input data and an objective variable corresponding to the input data is given, the input data is input to a plurality of basis functions, and an output value of each basis function is defined as an element A feature amount vector calculating unit for calculating a feature amount vector to be
A distribution adjusting unit that adjusts the distribution of the points so that the distribution of the points specified by the feature vector in the feature amount space approaches a predetermined distribution;
For a plurality of learning data, a function generation unit that generates an estimation function that outputs an estimated value of the objective variable in accordance with an input of the feature vector;
Comprising
Information processing device.

The distribution adjustment unit thins out the learning data so that a distribution of points specified by the feature vector in a feature space approaches a predetermined distribution;
The information processing apparatus according to claim 1.

The distribution adjusting unit weights the learning data so that a distribution of points specified by the feature vector in the feature space approaches a predetermined distribution;
The information processing apparatus according to claim 1.

The distribution adjustment unit thins the learning data so that the distribution of points specified by the feature vector in the feature amount space approaches a predetermined distribution, and for each of the learning data remaining after the thinning And weight,
The information processing apparatus according to claim 1.

The predetermined distribution is a uniform distribution or a Gaussian distribution.
The information processing apparatus according to any one of claims 1 to 4 .

When the distribution adjustment unit is additionally provided with new learning data, the distribution adjustment unit targets the learning data group including the new learning data and the existing learning data in the feature amount space. The learning data is thinned out so that the distribution of points specified by the quantity vector approaches a predetermined distribution.
The information processing apparatus according to claim 2 or 4 .

A basis function generation unit that generates the basis function by combining a plurality of functions prepared in advance;
The information processing apparatus according to any one of claims 1 to 6 .

The basis function generation unit updates the basis function based on a genetic algorithm,
When the basis function is updated, the feature amount vector calculation unit calculates the feature amount vector by inputting the input data to the updated basis function,
The function generation unit generates an estimation function that outputs an estimated value of the objective variable in accordance with an input of a feature vector calculated using the updated basis function;
The information processing apparatus according to claim 7.

When a plurality of learning data composed of input data and an objective variable corresponding to the input data is given, the input data is input to a plurality of basis functions, and an output value of each basis function is defined as an element Calculating a feature vector to be
Adjusting the distribution of the points so that the distribution of the points specified by the feature vector in the feature amount space approaches a predetermined distribution;
Generating an estimation function that outputs an estimated value of the objective variable with respect to an input of the feature quantity vector for a plurality of the learning data;
including,
Estimator generation method.

When a plurality of learning data composed of input data and an objective variable corresponding to the input data is given, the input data is input to a plurality of basis functions, and an output value of each basis function is defined as an element A feature vector calculation function for calculating a feature vector to be
A distribution adjusting function for adjusting the distribution of the points so that the distribution of the points specified by the feature vector in the feature amount space approaches a predetermined distribution;
A function generating function for generating an estimation function that outputs an estimated value of the objective variable in response to an input of the feature vector for a plurality of the learning data;
A program to make a computer realize.