This paper presents a methodology to calculate probabilities of failure using Probabilistic Support Vector Machines (PSVMs). Support Vector Machines (SVMs) have recently gained attention for reliability assessment because of several inherent advantages. Specifically, SVMs allow one to construct explicitly the boundary of a failure domain. In addition, they provide a technical solution for probl...

In natural language call routing, callers are routed to desired departments based on natural spoken responses to an open-ended “How may I direct your call?” prompt. Natural language call classification can be performed using support vector machines (SVMs) or the popular vector-based model used in information retrieval. We recently demonstrate how discriminative training is powerful to improve a...

Many learning algorithms approximately minimize a risk functional over a predefined function class. In order to establish consistency for such algorithms it is therefore necessary to know whether this function class approximates the Bayes risk. In this work we present necessary and sufficient conditions for the latter. We then apply these results to reproducing kernel Hilbert spaces used in sup...

In the aim of developing the assessment of speech disorders for detecting patients with Parkinson’s disease (PD), we have collected 34 sustained vowel / a /, from 34 subjects including 17 PD patients. We subsequently extracted from 1 to 20 coefficients of the Perceptual Linear Prediction (PLP) from each individual. To extract the voiceprint from each individual, we compressed the frames by calc...

We present new unsupervised and semi-supervised training algorithms for multi-class support vector machines based on semidefinite programming. Although support vector machines (SVMs) have been a dominant machine learning technique for the past decade, they have generally been applied to supervised learning problems. Developing unsupervised extensions to SVMs has in fact proved to be difficult. ...

Functional data analysis is a growing research field and numerous works present a generalization of the classical statistical methods to function classification or regression. In this paper, we focus on the problem of using Support Vector Machines (SVMs) for curve discrimination. We recall that important theoretical results for SVMs apply in functional space and propose simple functional kernel...

In this paper, a support vector machines (SVMs) based method is proposed for content-based audio classification and retrieval. Given a feature set, which in this work is composed of perceptual and cepstral feature, optimal class boundaries between classes are learned from training data by using SVMs. Matches are ranked by using distances from boundaries. Experiments are presented to compare var...

The aim of this paper is to afford classification tasks on asymmetric kernel matrices using Support Vector Machines (SVMs). Ordinary theory for SVMs requires to work with symmetric proximity matrices. In this work we examine the performance of several symmetrization methods in classification tasks. In addition we propose a new method that specifically takes classification labels into account to...

Support vector machines (SVMs) have shown great potential for learning classi®cation functions that can be applied to object recognition. In this work, we extend SVMs to model the appearance of human faces which undergo non-linear change across multiple views. The approach uses inherent factors in the nature of the input images and the SVM classi®cation algorithm to perform both multi-view face...