Support vector machine (SVM) is an optimal margin based classification technique in
machine learning. SVM is a binary linear classifier which has been extended to non-linear …

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV)
machines for function estimation. Furthermore, we include a summary of currently used …

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC).
However, manual evaluation of the diseased tissues under the microscope cannot reliably …

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function
(RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm …

We present a survey of the fundamentals and the applications of sparse grids, with a focus
on the solution of partial differential equations (PDEs). The sparse grid approach, introduced …

This chapter covers details of the support vector machine (SVM) technique, a sparse kernel
decision machine that avoids computing posterior probabilities when building its learning …

Instead of a standard support vector machine (SVM) that classifies points by assigning them
to one of two disjoint half-spaces, points are classified by assigning them to the closest of …

In this study a rigorous evaluation and comparison of the difference in water balance
simulations resulted from using different downscaling techniques, GCMs and hydrological …

Two-category support vector machines (SVM) have been very popular in the machine
learning community for classification problems. Solving multicategory problems by a series …

Most literature on support vector machines (SVMs) concentrates on the dual optimization
problem. In this chapter, we would like to point out that the primal problem can also be …