The separability theory of hyperbolic tangent kernels and support vector machines for pattern classification

Mathini Sellathurai, Simon Haykin

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

5 Citations (Scopus)

Abstract

A new theory is developed for the feature spaces of hyperbolic tangent used as an activation kernel for non-linear support vector machines. The theory developed herein is based on the distinct features of hyperbolic geometry, which leads to an interesting geometrical interpretation of the higher-dimensional feature spaces of neural networks using hyperbolic tangent as the activation function. The new theory is used to explain the separability of hyperbolic tangent kernels where we show that the separability is possible only for a certain class of hyperbolic kernels. Simulation results are given supporting the separability theory.

Original languageEnglish
Title of host publicationProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing
Pages1021-1024
Number of pages4
DOIs
Publication statusPublished - 1999
Event1999 IEEE International Conference on Acoustics, Speech, and Signal Processing - Phoenix, United States
Duration: 15 Mar 199919 Mar 1999

Conference

Conference1999 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 99
CountryUnited States
CityPhoenix
Period15/03/9919/03/99

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics

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