Generalised RBF networks trained using an IBL algorithm for mining symbolic data

Liviu Vladutu, Stergios Papadimitriou, Severina Mavroudi, Anastasios Bezerianos

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

Abstract

The application of neural networks to domains involving pre- diction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. Tra- ditional distances are inadequate to access the differences between the symbolic patterns. This work proposes the utilization of a statistically extracted distance measure in the context of Generalized Radial Basis Function (GRBF) networks. The main properties of the GRBF networks are retained in the new metric space. The regularization potential of these networks can be realized with this type of distance. Furthermore, the recent engineering of neural networks offers effective solutions for learning smooth functionals that lie on high dimensional spaces.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings
EditorsDavid Cheung, Graham J. Williams, Qing Li
PublisherSpringer Verlag
Pages587-598
Number of pages12
ISBN (Print)3540419101, 9783540419105
DOIs
StatePublished - Jan 1 2001
Event5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Kowloon, Hong Kong
Duration: Apr 16 2001Apr 18 2001

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2035
ISSN (Print)0302-9743

Other

Other5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001
CountryHong Kong
CityKowloon
Period4/16/014/18/01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Vladutu, L., Papadimitriou, S., Mavroudi, S., & Bezerianos, A. (2001). Generalised RBF networks trained using an IBL algorithm for mining symbolic data. In D. Cheung, G. J. Williams, & Q. Li (Eds.), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings (pp. 587-598). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_63