A hybrid projection-based and radial basis function architecture: Initial values and global optimisation

S. Cohen, N. Intrator

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a mechanism for constructing and training a hybrid architecture of projection-based units and radial basis functions. In particular, we introduce an optimisation scheme which includes several steps and assures a convergence to a useful solution. During network architecture construction and training, it is determined whether a unit should be removed or replaced. The resulting architecture often has a smaller number of units compared with competing architectures. A specific overfitting resulting from shrinkage of the RBF radii is addressed by introducing a penalty on small radii. Classification and regression results are demonstrated on various benchmark data sets and compared with several variants of RBF networks [1,2]. A striking performance improvement is achieved on the vowel data set [3].

Original languageEnglish
Pages (from-to)113-120
Number of pages8
JournalPattern Analysis and Applications
Volume5
Issue number2
DOIs
StatePublished - 2002

Keywords

  • Clustering
  • Hybrid network architecture
  • Projection units
  • RBF units
  • Regularisation
  • SMLP

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