Generalization bounds for the regression of real-valued functions

Rhee Man Kil, Imhoi Koo

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

4 Scopus citations

Abstract

The paper suggests a new bound of estimating the confidence interval defined by the absolute value of difference between the true (or general) and empirical risks for the regression of real-valued functions. The theoretical bounds of confidence intervals can be derived in the sense of probably approximately correct (PAC) learning. However, these theoretical bounds are too overestimated and not well fitted to the empirical data. In this sense, a new bound of the confidence interval which can explain the behavior of learning machines more faithfully to the given samples, is suggested.

Original languageEnglish (US)
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
EditorsKunihiko Fukushima, Lipo Wang, Jagath C. Rajapakse, Soo-Young Lee, Xin Yao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1766-1770
Number of pages5
ISBN (Electronic)9810475241, 9789810475246
DOIs
StatePublished - 2002
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: Nov 18 2002Nov 22 2002

Publication series

NameICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
Volume4

Other

Other9th International Conference on Neural Information Processing, ICONIP 2002
Country/TerritorySingapore
CitySingapore
Period11/18/0211/22/02

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Fingerprint

Dive into the research topics of 'Generalization bounds for the regression of real-valued functions'. Together they form a unique fingerprint.

Cite this