A Hybrid Projection Based and Radial Basis Function Architecture

We introduce a mechanism for constructing and training a hybrid architecture of projection based units and radial basis functions. In particular, we introduce an optimization 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 has often 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, 12]. A striking performance improvement is achieved on the vowel data set [8].

• Type: Conference
• Year: 2000
• Where: MCS
• Authors: Shimon Cohen, Nathan Intrator
• Keywords: MCS 2000, Pattern Recognition, Projection Based Units, Radial Basis Functions, RBF Radii