A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Neural Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Choice of Basis for Laplace Approximation
Machine Learning
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Multiclass Classification Method Based on Multiple Pairwise Classifiers
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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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.