NOUMIR , Zineb

In this paper, we investigate a novel online one-class classification method. We consider a least-squares optimization problem, where the model complexity is controlled by the coherence criterion as a sparsification rule. This criterion is coupled with a simple updating rule for online learning, which yields a low computational demanding...

The one-class classification problemis often addressed by solving a constrained quadratic optimization problem, in the same spirit as support vector machines. In this paper, we derive a novel one-class classification approach, by investigating an original sparsification criterion. This criterion, known as the coherence criterion, is based on a...

In this paper, we study the multiclass classification problem. We derive a framework to solve this problem by providing algorithms with the complexity of a single binary classifier. The resulting multiclass machines can be decomposed into two categories. The first category corresponds to vector-output machines, where we develop several...

The one-class classification has been successfully applied in many communication, signal processing, and machine learning tasks. This problem, as defined by the one-class SVM approach, consists in identifying a sphere enclosing all (or the most) of the data. The classical strategy to solve the problem considers a simultaneous estimation of both...

In this paper, we derive an adaptive one-class classification algorithm. We propose a least-squares formulation of the problem, where the model complexity is controlled by a parsimony criterion. We consider the linear approximation criterion, and we couple it with a simple adaptive updating algorithm for online learning. We conduct experiments...

This paper deals with multi-class classification problems. Many methods extend binary classifiers to operate a multi-class task, with strategies such as the one-vs-one and the one-vs-all schemes. However, the computational cost of such techniques is highly dependent on the number of available classes. We present a method for multi-class...

Many processes exhibit exponential behavior. When kernel-based machines are applied on this type of data, conventional kernels such as the Gaussian kernel are not appropriate. In this paper, we derive kernels adapted to time series of exponential decay or growth processes. We provide a theoretical study of these kernels, including the issue of...

Cet article traite du problème de classification multi-classe en reconnaissance des formes. La résolution de ce type de problèmes nécessite des algorithmes au coût calculatoire souvent beaucoup plus élevé que les méthodes d'apprentissage dédiées à la classification binaire. On propose dans cet article une nouvelle formulation pour la conception...

International audience

International audience