Presentation	2006-05-18 New Approaches to the Relation between Computational Learning and Machine Learning Akihiro YAMAMOTO,
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Abstract(in English)	The relation between Logic and Machine Learning is investigated for a long time. The major approach to the goal is using logic in learning: using syntax of logic for representation of data and knowledge, applying automated theorem proving to keeping consistency of hypotheses to given examples, and guaranteeing the appropriateness of learning with semantics of logic. Recently researches on new approaches to the relation have appeared. In this survey we introduce such new researches, in particular, relation between logic and parameter estimation, relation between logic and data-mining, application of logic to the Support Vector Machine, and Machine Learning in Computational Algebra.
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Keyword(in English)	Computational Logic / Machine Learning / Inductive Logic Programming
Paper #	AI2006-7
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Conference Information
Committee	AI
Conference Date	2006/5/11(1days)
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Paper Information
Registration To	Artificial Intelligence and Knowledge-Based Processing (AI)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	New Approaches to the Relation between Computational Learning and Machine Learning
Sub Title (in English)
Keyword(1)	Computational Logic
Keyword(2)	Machine Learning
Keyword(3)	Inductive Logic Programming
1st Author's Name	Akihiro YAMAMOTO
1st Author's Affiliation	Graduate School of Informatics, Kyoto University()
Date	2006-05-18
Paper #	AI2006-7
Volume (vol)	vol.106
Number (no)	38
Page	pp.pp.-
#Pages	6
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