Pattern Analysis & Applications

, Volume 5, Issue 2, pp 113-120

First online:

A Hybrid Projection-based and Radial Basis Function Architecture: Initial Values and Global Optimisation

  • S. CohenAffiliated withSchool of Computer Science, Tel-Aviv University, Ramat Aviv, Israel
  • , N. IntratorAffiliated withSchool of Computer Science, Tel-Aviv University, Ramat Aviv, Israel

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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].

Key words: Clustering; Hybrid network architecture; Projection units; RBF units; Regularisation; SMLP