Learning from Expert Hypotheses and Training Examples

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 1997-12-25

著者

• Akiba Yasuhiro

  Ntt Communication Science Laboratories

• Kaneda Shigeo

  Ntt Communication Science Laboratories

• ALMUALLIM Hussein

  King Fahd University of Petroleum and Minerals

• ISHII Megumi

  NTT Communication Science Laboratories

関連論文

• Learning from Expert Hypotheses and Training Examples
• Induction of Japanese-English Translation Rules from Ambiguous Examples and a Large Semantic Hierarchy
• 仮事例生成を用いた知識と事例からの学習

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