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 ...

this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...

This paper presents a classification algorithm based on Support Vector Machines classifiers combined with Boosting techniques. This classifier presents a better performance in training time, a similar generalization and a similar model complexity but the model representation is more compact.

In many problems of machine learning, the data are distributed nonlinearly. One way to address this kind of data is training a nonlinear classifier such as kernel support vector machine (kernel SVM). However, the computational burden of kernel SVM limits its application to large scale datasets. In this paper, we propose a Clustered Support Vector Machine (CSVM), which tackles the data in a divi...

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...

This paper proposes a mathematical programming framew ork for combining SVMs with possibly di erent kernels. Compared to single SVMs, the advantage of this approach is tw ofold: it creates SVMs with local domains of expertise leading to local enlargements of the margin, and it allows the use of simple linear kernels combined with a xed boolean operation that is particularly well suited for buil...

Support Vector Machines (SVMs) are state-of-the-art algorithms for classification in machine learning. However, the SVM formulation does not directly seek to find sparse solutions. In this work, we propose an alternate formulation that explicitly imposes sparsity. We show that the proposed technique is related to the standard SVM formulation and therefore shares similar theoretical guarantees. ...

In this work we first propose a heteroscedastic generalization to RVM, a fast Bayesian framework for regression, based on some recent similar works. We use variational approximation and expectation propagation to tackle the problem. The work is still under progress and we are examining the results and comparing with the previous works.