This paper concerns intrusion detection and audit trail reduction. We describe approaches to intrusion detection and audit data reduction using support vector machines and neural networks. Using a set of benchmark data from the KDD (Knowledge Discovery and Data Mining) competition designed by DARPA, we demonstrate that efficient and highly accurate classifiers can be built using either support ...

Because of Polarimetric Synthetic Aperture Radar (PolSAR) contains the different features which relate to the physical properties of the terrain in unique ways, polarimetric imagery provides an efficient tool for the classification of the complex geographical areas. Support Vector Machines (SVMs) are particularly attractive in the remote sensing field due to their ability to handle the nonlinea...

Support vector machines (SVMs) play a very dominant role in data classification due to their good generalization performance. However, they suffer from the high computational complexity in the classification phase when there are a considerable number of support vectors (SVs). Then it is desirable to design efficient algorithms in the classification phase to deal with the datasets of realtime pa...

Affective computation allows machines to express and recognize emotions, a core component of computer games. A natural way to express emotion is language, through text and speech; computational methods that accurately recognize emotion in text and speech are therefore important. Machine learning techniques such as support vector machines (SVMs) have been used successfully for topic detection in...

Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. ...

A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines SVMs have been applied to the problem of financial early-warning prediction Rose, 1999 . The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the ...

various statistical methods have been proposed in terms of predicting the outcomes of facing special factors. in the classical approaches,  making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. however, most of the times enough information about the probability distribution of studied variables is not available to the ...

in this paper, we present a new brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (ls-svm). the method consists of three steps. in the first two steps, the skull is removed and cerebrospinal fluid (csf) is extracted. these two steps are performed using the fast toolbox (fmrib's a...

In this paper, we present a comprehensive survey on applications of Support Vector Machines (SVMs) for pattern recognition. Since SVMs show good generalization performance on many real-life data and the approach is properly motivated theoretically, it has been applied to wide range of applications. This paper describes a brief introduction of SVMs and summarizes its numerous applications.

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a SVMs based algorithm for ...