Penalized learning as multiple object optimization

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Place of PublicationPiscataway, NJ, United States
Number of pages6
Publication statusPublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: 1994 Jun 271994 Jun 29


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

ASJC Scopus subject areas

  • Software


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