Support Vector Machine Architecture

Introduction to SVM

A support vector machine (SVM) is a computer algorithm that classifies a given example by assigning labels to objects through a number of training examples (William, 2006). This algorithm consists of classification and regression algorithms, which were developed by Vapnik and it is gaining popularity due to many attractive features, and its promising empirical performance. For instance, an SVM can be used in the game development by clustering around the graphics into 3D graphic. Alternatively, an SVM can detect handwritten digits by examine large collection of scanned images of handwritten zeroes, ones and so forth (William, 2006).

SVM algorithms are often based on the Structural Risk Minimization (SRM) principle from statistical learning theory. The role of the SRM is to find an optimal hyper lane for which the lowest true error can be guaranteed. This framework has developed into an e learning algorithm when trained from a finite data set, and formed the ‘true’ performance when used in practice.

For a details explaination on how SVM work, you can download this project which written by me for my Final Year Project at http://rapidshare.com/files/253305852/SVM.docx.html.Anyway, please quote a reference, if you want to take this for future research & development. Thank you.

If you have problem downloading the file, please kindly send me your email and I will post it to you. Cheers =)