Exact learning Boolean functions via the monotone theory
Information and Computation
Artificial Intelligence
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
A Logical Representation for Relevance Criteria
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
A Case Base Similarity Framework
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Analysis of Case-Based Representability of Boolean Functions by Monotone Theory
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Protos: a unified approach to concept representation, classification, and learning
Protos: a unified approach to concept representation, classification, and learning
Constructing a Critical Casebase to Represent a Lattice-Based Relation
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
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In this paper, we propose a learning method of minimal case-base to represent taxonomic relation in a tree-structured concept hierarchy. We firstly propose case-based taxonomic reasoning and show an upper bound of necessary positive cases and negative cases to represent a relation. Then, we give an learning method of a minimal casebase with sampling and membership queries. We analyze this learning method by sample complexity and query complexity in the framework of PAC learning.