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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)
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)
—Calculating Euclidean distance matrix is a data intensive operation and becomes computationally prohibitive for large datasets. Recent development of Graphics Processing Units (GPUs) has produced superb performance on scientific computing problems using massive parallel processing cores. However, due to the limited size of device memory, many GPU based(More)