Mutual information gaining algorithm and its relation to PAC-learning algorithm

Eiji Takimoto, Ichiro Tajika, Akira Maruoka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, the mutual information between a target concept and a hypothesis is used to measure the goodness of the hypothesis rather than the accuracy, and a notion of mutual information gaining (Mi-gaining) algorithms is introduced. In particular, strong and weak Mi-gaining algorithms are defined depending on the amount of information acquired, and their relation to strong and weak PAC-learning algorithms are investigated. It is shown that although a strong Mi-gaining algorithm is equivalent to a strong PAC-learning algorithm, a weak MI- gaining algorithm does not necessarily imply a weak PAC-learning algorithm, and vice versa. Moreover, a general boosting scheme for weak Mi-gaining algorithms is given. That is, any weak Mi-gaining algorithm can be used to build a strong one. Since a strong Mi-gaining algorithm is also a strong PAC-learning algorithm, the result can be viewed to give a sufficient condition for a class of algorithms to be boosted into strong learning algorithms.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994, Proceedings
EditorsSetsuo Arikawa, Klaus P. Jantke
PublisherSpringer Verlag
Pages547-559
Number of pages13
ISBN (Print)9783540585206
Publication statusPublished - Jan 1 1994
Externally publishedYes
Event4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994 - Reinhardsbrunn Castle, Germany
Duration: Oct 10 1994Oct 15 1994

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume872 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994
CountryGermany
CityReinhardsbrunn Castle
Period10/10/9410/15/94

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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