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Learning from Expert Hypotheses and Training Examples
Shigeo KANEDA
Hussein ALMUALLIM
Yasuhiro AKIBA
Megumi ISHII
Publication
IEICE TRANSACTIONS on Information and Systems Vol.E80-D No.12 pp.1205-1214
Publication Date: 1997/12/20
Online ISSN:
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence and Cognitive Science
Keyword:
machine learning,
decision tree,
expert hypotheses,
training examples,
Full Text: PDF
Summary:
We present a method for learning classification functions from pre-classified training examples and hypotheses written roughly by experts. The goal is to produce a classification function that has higher accuracy than either the expert's hypotheses or the classification function inductively learned from the training examples alone. The key idea in our proposed approach is to let the expert's hypotheses influence the process of learning inductively from the training examples. Experimental results are presented demonstrating the power of our approach in a variety of domains.
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