Learning Multi-category Classification in Bayesian Framework

Abstract. We propose an algorithm for Sparse Bayesian Classification for multi-class problems using Automatic Relevance Determination(ARD). Unlike other approaches which treat multiclass problem as multiple independent binary classification problem, we propose a method to learn the multiclass predictor directly. The usual approach of "one against rest" and "pairwise coupling" are not only computationally demanding during training stage but also generates dense classifiers which have greater tendency to overfit and have higher classification cost. In this paper we discuss the algorithmic implementation of Multiclass Classification model and compare it with other multi-class classifiers. We also empirically evaluate the classifier on viewpoint learning problem using features extracted from human silhouettes. Our experiments show that our algorithm generates sparser classifiers, with performance comparable to state-of-the-art multi-class classifier. 1 Motivation and Re...

• Type: Conference
• Year: 2006
• Where: ACCV
• Authors: Atul Kanaujia, Dimitris N. Metaxas
• Keywords: ACCV 2006, Binary Classification Problems, Computer Vision, Independent Binary Classification, Multiclass Classification