A multiclass kernel perceptron algorithm

Original kernel machines (e.g., support vector machine, least squares support vector machine, kernel Fisher discriminant analysis, kernel perceptron algorithm, and etc.) were mainly designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research issue. Rosenblatt´s linear perceptron algorithm for binary classification and its corresponding multiclass linear version are the simplest learning machines according to their algorithmic routines. Kernel perceptron algorithm for binary classification was constructed by extending linear perceptron algorithm with Mercer kernel. In this paper, a multiclass kernel perceptron algorithm is proposed by combining multiclass linear perceptron algorithm with binary kernel perceptron algorithm, which can deal with multiclass classification problem directly and nonlinearly in a simple iterative procedure. Two artificial examples and four benchmark datasets are used to evaluate the performance of our multiclass method. The experimental results show that our algorithm could achieve the good classification performance