Penalized learning as multiple object optimization

Yasuo Matsuyama, Haruo Nakayama, Taketoshi Sasai, Yuh Perng Chen

研究成果: Conference contribution

抄録

Learning algorithms guided by costs with a variety of penalties are discussed. Both unsupervised and supervised cases are addressed. The penalties are added and/or multiplied to the basic error measure. Since these extra penalties include combination parameters with respect to the basic error, the total problem belongs to a class of multiple object optimization. Learning algorithms on general cases are derived first. Then, individual cases such as penalties on undesirable weights and outputs are treated. A method to find a preferred solution among the Pareto optimal set of the multiple object optimization is given.

本文言語English
ホスト出版物のタイトルIEEE International Conference on Neural Networks - Conference Proceedings
Place of PublicationPiscataway, NJ, United States
出版社IEEE
ページ187-192
ページ数6
1
出版ステータスPublished - 1994
外部発表はい
イベントProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
継続期間: 1994 6 271994 6 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period94/6/2794/6/29

ASJC Scopus subject areas

  • Software

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