Seminar on Computational Learning and
Adaptation
Learning Taxonomic Relations by Case-based Reasoning
Ken Satoh
Professor, Foundations of Information Research Division
National Institute of Informatics, Japan
ksatoh@nii.ac.jp
In this paper, we propose a learning method that requires a minimal case base to
represent taxonomic relations in a tree-structured concept hierarchy.
We firstly propose case-based taxonomic reasoning
and show an upper bound on the necessary positive cases and
negative cases to represent a relation. Then, we describe a learning
method that requires a minimal case base, with sampling and membership queries.
We analyze this learning method by sample complexity and query complexity
in the framework of PAC learning. This is an extension of work
on approximating a critical case base which represents a boolean function
using set-inclusion based similarity. The work is related to "monotone
theory", developed by Bshouty, Kardon and Roth.
Date: Thursday, February 21
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Time: 4:15-5:30PM
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Place: Cordura 100
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