Kernel matching pursuit classifier ensemble

摘要

Kernel Matching Pursuit Classifier (KMPC), a novel classification machine in pattern recognition, has an excellent advantage in solving classification problems for the sparsity of the solution. Unfortunately, the performance of the KMPC is far from the theoretically expected level of it. Ensemble Methods are learning algorithms that construct a collection of individual classifiers which are independent and yet accurate, and then classify a new data point by taking vote of their predictions. In such a way, the performance of classifiers can be improved greatly. In this paper, on a thorough investigation into the principle of KMPC and Ensemble Method, we expatiate on the theory of KMPC ensemble and pointed out the ways to construct it. The experiments performed on the artificial data and UCI data show KMPC ensemble combines the advantages of KMPC with ensemble method, and improves classification performance remarkably.