A Divide-and-Cooperate Machine Learning Model-Based RBF with Its VC Dimension Analysis

Abstract

In this paper, a Divide-and-Cooperate Machine Learning Model (DCML) based Radial Basis Function Network (RBF) is constructed. This DCML is composed of several sub-RBF networks that take some variables as their inputs. The output of DCML is the sum of sub-RBF networks' outputs. The analysis of VC dimension of DCML shows in theory that its structural complexity is less than conventional Extended Radial Basis Function Network (ENRBF). The experimental results have verified that the DCML outperforms conventional ENRBF.