Relevance units machine for classification

Classification, a task to assign each input instance to a discrete class label, is a prevailing problem in various areas of study. A great amount of research for developing models for classification has been conducted in machine learning research and recently, kernel-based approaches have drawn considerable attention mainly due to their superiority on generalization and computational efficiency in prediction. In this work, we present a new sparse classification model that integrates the basic theory of a sparse kernel learning model for regression, called relevance units machine, with the generalized linear model. A learning algorithm for the proposed model will be described, followed by experimental analysis comparing its predictive performance on benchmark datasets with that of the support vector machine and relevance vector machine, the two most popular methods for kernel-based classification.