We present a discriminative training algorithm, that uses support vector machines (SVMs), to improve the classification of discrete and continuous output probability hidden Markov ...
We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow...
Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshe...
Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multi...
In this paper an effective method of using SVM classifier for multiple feature classification is proposed. Compared with traditional combination methods where all needed base clas...
Support vector machines (SVMs) have proven to be a powerful technique for pattern classification. SVMs map inputs into a high dimensional space and then separate classes with a hy...
William M. Campbell, Joseph P. Campbell, Douglas A...