著者

Yamaguti Yutaka Kuroda Shigeru Fukushima Yasuhiro Tsukada Minoru Tsuda Ichiro

出版者

Elsevier

雑誌

Neural Networks (ISSN:08936080)

巻号頁・発行日

vol.24, no.1, pp.43-53, 2011

被引用文献数

16

著者

Hino Hideitsu Murata Noboru

出版者

Elsevier Ltd.

雑誌

Neural networks (ISSN:08936080)

巻号頁・発行日

vol.46, pp.260-275, 2013-10

被引用文献数

11 2

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The Shannon information content is a valuable numerical characteristic of probability distributions. The problem of estimating the information content from an observed dataset is very important in the fields of statistics, information theory, and machine learning. The contribution of the present paper is in proposing information estimators, and showing some of their applications. When the given data are associated with weights, each datum contributes differently to the empirical average of statistics. The proposed estimators can deal with this kind of weighted data. Similar to other conventional methods, the proposed information estimator contains a parameter to be tuned, and is computationally expensive. To overcome these problems, the proposed estimator is further modified so that it is more computationally efficient and has no tuning parameter. The proposed methods are also extended so as to estimate the cross-entropy, entropy, and Kullback–Leibler divergence. Simple numerical experiments show that the information estimators work properly. Then, the estimators are applied to two specific problems, distribution-preserving data compression, and weight optimization for ensemble regression.