… “support vector” when the corresponding coefficient αi is non zero. The number s of support
vectors … Fast training of support vector machines using sequential minimal optimization. In B. …

… Abstract In this study we introduce a generalized support vector classification problem: Let
Xi, i = 1,...,n be mutually exclusive sets of pattern vectors such that all pattern vectors xi,k, k = 1,…

… detection scheme based on Support Vector Machines (SVMs) and sliding windows. We
find that our system can detect black hole attacks and selective forwarding attacks with high …

… However, if there exists a method of assigning the desired level of selectivity a priori, rang…
selectivity into the Relevance Kernel Machine (RKM) [4,5] and Support Kernel Machine (SKM) […

… In this paper, we review remote sensing implementations of support vector machines (SVMs),
a promising machine learning methodology. This review is timely due to the exponentially …

… Support vector machines (SVMs) are a supervised classifier successfully applied in a plethora
of real-life applications. However, they suffer from the important shortcomings of their high …

… able to prioritize selective compounds and separate selective and nonselective compounds
… human adenosine receptor subtypes and to predict compound selectivity. In ct-SVM, binary …

… In this chapter, we explore Support Vector Machine (SVM)—a machine learning method
that has become exceedingly popular for neuroimaging analysis in recent years. Because of …

… As an initial step, we describe here an approach based on Support Vector Machine (SVM) …
Results: We demonstrate that SVMs with selective kernel scaling are an effective tool in …

… support vector machine (SVM) was applied for the prediction of selectivity coefficients of
anion-selective … are responsible for the selectivity coefficient of anion-selective electrode for …