Large-scale classiication is a very active research line in data mining. It can be applied to problems like credit card fraud detection or content-based document browsing. In recent years, several eecient algorithms for this area have been proposed by Mangasarian and Musicant. These approaches, based on quadratic problems, are: Successive OverRelaxation (SOR), Active Support Vector Machines (AS...

In contrast to learning a general prediction rule, V. Vapnik proposed the transductive learning setting where predictions are made only at a fixed number of known test points. This allows the learning algorithm to exploit the location of the test points, making it a particular type of semi-supervised learning problem. Transductive support vector machines (TSVMs) implement the idea of transducti...

Many problems require making sequential decisions. For these problems, the benefit of acquiring further information must be weighed against the costs. In this paper, we describe the catenary support vector machine (catSVM), a margin-based method to solve sequential stopping problems. We provide theoretical guarantees for catSVM on future testing examples. We evaluated the performance of catSVM ...

Most Support Vector (SV) methods proposed in the recent literature can be viewed in a uni ed framework with great exibility in terms of the choice of the basis functions. We show that all these problems can be solved within a unique approach if we are equipped with a robust method for nding a sparse solution of a linear system. Moreover, for such a purpose, we propose an iterative algorithm tha...

An implicit Lagrangian for the dual of a simple reformulation of the standard quadratic program of a linear support vector machine is proposed. This leads to the minimization of an unconstrained differentiable convex function in a space of dimensionality equal to the number of classified points. This problem is solvable by an extremely simple linearly convergent Lagrangian support vector machin...

We introduce in this paper Fβ SVMs, a new parametrization of support vector machines. It allows to optimize a SVM in terms of Fβ , a classical information retrieval criterion, instead of the usual classification rate. Experiments illustrate the advantages of this approach with respect to the traditionnal 2norm soft-margin SVM when precision and recall are of unequal importance. An automatic mod...

By setting apart the two functions of a support vector machine: separation of points by a nonlinear surface in the original space of patterns, and maximizing the distance between separating planes in a higher dimensional space, we are able to deene indeenite, possibly discontinuous, kernels, not necessarily inner product ones, that generate highly nonlin-ear separating surfaces. Maximizing the ...

The Support Vector Machine (SVM) has shown great performance in practice as a classification methodology. Oftentimes multicategory problems have been treated as a series of binary problems in the SVM paradigm. Even though the SVM implements the optimal classification rule asymptotically in the binary case, solutions to a series of binary problems may not be optimal for the original multicategor...

In ranking problems, the goal is to learn a ranking function r(x) ∈ R from labeled pairs x, x′ of input points. In this paper, we consider the related comparison problem, where the label y ∈ {−1, 0, 1} indicates which element of the pair is better, or if there is no significant difference. We cast the learning problem as a margin maximization, and show that it can be solved by converting it to ...