Transcranial doppler is a test that measures the velocity of blood flow through the brain’s blood vessels. It is used to help in the diagnosis of emboli, stenosis, vasospasm from a subarachnoid hemorrhage (bleeding from a ruptured aneurysm), and other problems. It is often used in conjunction with other tests such as MRI, MRA, carotid duplex ultrasound, and CT scans.
Published in Chapter:
Support Vector Machines in Neuroscience
Onur Seref (University of Florida, USA), O. Erhun Kundakcioglu (University of Florida, USA), and Michael Bewernitz (University of Florida, USA)
Copyright: © 2008
|Pages: 11
DOI: 10.4018/978-1-59904-889-5.ch161
Abstract
The underlying optimization problem for the maximal margin classifier is only feasible if the two classes of pattern vectors are linearly separable. However, most of the real life classification problems are not linearly separable. Nevertheless, the maximal margin classifier encompasses the fundamental methods used in standard SVM classifiers. The solution to the optimization problem in the maximal margin classifier minimizes the bound on the generalization error (Vapnik, 1998). The basic premise of this method lies in the minimization of a convex optimization problem with linear inequality constraints, which can be solved efficiently by many alternative methods (Bennett & Campbell, 2000).