Decision boundary learning based on an improved PSO algorithm

The goal of this research is to design a multimedia analyzer (MA) that can be embedded in portable devices. This MA can recognize different multimedia (e.g. text and image) patterns and help the user to analyze the multimedia contents more efficiently. To realize the MA in an environment with limited computing resource, we propose a new concept called decision boundary learning (DBL). The basic idea is to generate training patterns close to the decision boundary (DB), so that a neural network (NN) with high generalization ability can be obtained. In this paper, the DB is first obtained approximately using a support vector machine (SVM), and the desired training patterns are found using an improved particle swarm optimization (PSO) algorithm. Experimental results show that the NNs so obtained are comparable in performance to the SVMs although the former are much more compact.