In this paper, the problem of classification of defects occurring in a textile manufacture is addressed. A new classification scheme is devised in which different features, extrac...
Support vector machines (SVMs) are an extremely successful type of classification and regression algorithms. Building an SVM entails solving a constrained convex quadratic program...
Classification problems in critical applications such as health care or security often require very high reliability because of the high costs of errors. In order to achieve this r...
Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification a...
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...