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We present an algorithm for the application of support vector machine (SVM) learning to image compression. The algorithm combines SVMs with the discrete cosine transform (DCT). Unlike a classic radial basis function networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of(More)
This chapter describes an active-set algorithm for the solution of quadratic programming problems in the context of Support Vector Machines (SVMs). Most of the common SVM optimizers implement working-set algorithms like the SMO method because of their ability to handle large data sets. Although they show generally good results, they may perform weakly in(More)
OBJECTIVE To improve the performance of gene extraction for cancer diagnosis by recursive feature elimination with support vector machines (RFE-SVMs): A cancer diagnosis by using the DNA microarray data faces many challenges the most serious one being the presence of thousands of genes and only several dozens (at the best) of patient's samples. Thus, making(More)