On the complexity of hypothesis space and the sample complexity for machine learning

Makoto Nakazawa, Toshiyuki Kohnosu, Toshiyasu Matsushima, Shigeichi Hirasawa

研究成果: Conference article査読

抄録

The problem of learning a concept from examples in the model introduced by Valiant is discussed. According to the traditional ways of thinking, it is assumed that the learnability is independent of the occurrence probability of instance. By utilizing this probability, we propose the metric as a new measure to determine the complexity of hypothesis space. The metric measures the hardness of discrimination between hypotheses. Furthermore, we obtain the average metric dependent on prior information. This metric is the measure of complexity for hypothesis space in the average. Similarly in the worst case, we obtain the minimum metric. We make clear the relationship between these measures and the Vapnik - Chervonenkis (VC) dimension. Finally, we show the upper bound on sample complexity utilizing the metric. This results can be applied in the discussion on the learnability of the class with an infinite VC dimension.

本文言語English
ページ(範囲)132-137
ページ数6
ジャーナルProceedings of the IEEE International Conference on Systems, Man and Cybernetics
1
出版ステータスPublished - 1994
イベントProceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics. Part 1 (of 3) - San Antonio, TX, USA
継続期間: 1994 10 21994 10 5

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ハードウェアとアーキテクチャ

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