Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik’s statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques. The SVMs operate within the framework of regularization theory by minimizing an empirical risk in a...

EMPs Extended morphological profiles EMPs Extended morphological profiles LDA Linear discriminant analysis LogDA Logarithmic discriminant analysis MLR Multinomial logistic regression MLRsubMRF Subspace-based multinomial logistic regression followed by Markov random fields MPs Morphological profiles MRFs Markov random fields PCA Principal component analysis QDA Quadratic discriminant analysis RH...

The linear discriminant analysis based on the generalized singular value decomposition (LDA/GSVD) has recently been introduced to circumvents the nonsingularity restriction that occur in the classical LDA so that a dimension reducing transformation can be effectively obtained for undersampled problems. In this paper, relationships between support vector machines (SVMs) and the generalized linea...

In this paper we e v aluate the performance of Support Vector Machines SVMs and Multi-Layer Perceptrons MLPs on two diierent problems of Particle Identiication in High Energy Physics experiments. The obtained results indicate that SVMs and MLPs tend to perform very similarly.

each license plate recognition system is composed of three main parts, namely, license plate detection, character segmentation and character recognition. in this paper, we focus on the improvement and innovation of the character recognition step. for this purpose, a new hierarchical architecture based on support vector machines (svms) is suggested for persian license plate characters recognitio...

Support Vector Machines have received considerable attention from the pattern recognition community in recent years. They have been applied to various classical recognition problems achieving comparable or even superior results to classifiers such as neural networks. We investigate the application of Support Vector Machines (SVMs) to the problem of road recognition from remotely sensed images u...

Many cellular functions are carried out in compartments of the cell. The cellular localization of a protein is thus related to its function identification. This paper investigates the use of two Machine Learning techniques, Support Vector Machines (SVMs) and Decision Trees (DTs), in the protein cellular localization prediction problem. Since the given task has multiple classes and SVMs are orig...

In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set. Due to Mercer's conditions on the kernels, the corresponding optimization problems are convex and hence have no local minima....

In this paper we evaluate the performance of Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs) on two diierent problems of Particle Identiication in High Energy Physics experiments. The obtained results indicate that SVMs and MLPs tend to perform very similarly.

It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.