Presentation 1994/7/23
On the Complexity of Hypothesis Space and the Sample Complexity for Machine Learning
Makoto Nakazawa, Toshiyasu Matsushima, Shigeichi Hirasawa,
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Abstract(in English) 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 theoccurence probability qf 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.
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Keyword(in English) Concept learning / learning from example / PAC learning / sample complexity / VC-dimension
Paper # IT94-42
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Committee IT
Conference Date 1994/7/23(1days)
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Registration To Information Theory (IT)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) On the Complexity of Hypothesis Space and the Sample Complexity for Machine Learning
Sub Title (in English)
Keyword(1) Concept learning
Keyword(2) learning from example
Keyword(3) PAC learning
Keyword(4) sample complexity
Keyword(5) VC-dimension
1st Author's Name Makoto Nakazawa
1st Author's Affiliation Department of Industrial Engineering and Management,School of Science and Engineering,Waseda University()
2nd Author's Name Toshiyasu Matsushima
2nd Author's Affiliation Department of Industrial Engineering and Management,School of Science and Engineering,Waseda University
3rd Author's Name Shigeichi Hirasawa
3rd Author's Affiliation Department of Industrial Engineering and Management,School of Science and Engineering,Waseda University
Date 1994/7/23
Paper # IT94-42
Volume (vol) vol.94
Number (no) 171
Page pp.pp.-
#Pages 6
Date of Issue