submit urlsubmit rss feedadd directory

article

Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Their common factor is the use of a technique known as the "kernel trick" to apply linear classification techniques to non-linear classification problems.

Linear classification


Motivation

Suppose we want to classify some data points into two classes. Often we are interested in classifying data as part of a machine-learning process. These data points may not necessarily be points in R2 but may be multidimensional Rp (statistics notation) or Rn (computer science notation) points. We are interested in whether we can separate them by a hyperplane. As we examine a hyperplane, this form of classification is known as linear classification. We also want to choose a hyperplane that separates the data points "neatly", with maximum distance to the closest data point from both classes -- this distance is called the margin. We desire this property since if we add another data point to the points we already have, we can more accurately classify the new point since the separation between the two classes is greater. Now, if such a hyperplane exists, the hyperplane is clearly of interest and is known as the maximum-margin hyperplane or the optimal hyperplane, as are the vectors that are closest to this hyperplane, which are called the support vectors.

Formalization

We consider data points of the form: \{ (\mathbf{x}_1, c_1), (\mathbf{x}_2, c_2), \ldots, (\mathbf{x}_n, c_n)\} where the ci is either 1 or −1 -- this constant denotes the class to which the point \mathbf{x}_i belongs. Each \mathbf{x}_i is a p- (statistics notation), or n- (computer science notation) dimensional vector of scaled or [-1,1 values. The scaling is important to guard against variables (attributes) with larger variance that might otherwise dominate the classification. We can view this as training data, which denotes the correct classification which we would like the SVM to eventually distinguish, by means of the dividing hyperplane, which takes the form
\mathbf{w}\cdot\mathbf{x} - b=0.

More on [ Support vector machine ]


directory of related categories

 

 
Support_Vector_Machines RSS feed
Support Vector Machines - Twitter Search

Support Vector Machines In PHP http://bit.ly/92sVG9 #postrank #php
pr_php (PostRank – PHP) Sat, 12 Dec 2009 01:17:16 -0000
Support Vector Machines In PHP http://bit.ly/92sVG9 #postrank #php
has a clementine to eat.... yay fruit! Boo Support Vector Machines! #fruit #AI
Kris_Ether (Christopher Handley) Thu, 10 Dec 2009 15:47:34 -0000
has a clementine to eat.... yay fruit! Boo Support Vector Machines! #fruit #AI
LIBSVM -- A Library for Support Vector Machines http://bit.ly/SOAVz
rstuven (Ricardo Stuven) Thu, 10 Dec 2009 09:48:57 -0000
LIBSVM -- A Library for Support Vector Machines http://bit.ly/SOAVz
Support Vector Machines Portal http://bit.ly/5hza6j
rstuven (Ricardo Stuven) Thu, 10 Dec 2009 09:48:56 -0000
Support Vector Machines Portal http://bit.ly/5hza6j
A Tutorial on Support Vector Machines for Pattern Recognition http://bit.ly/5jHlS3
rstuven (Ricardo Stuven) Thu, 10 Dec 2009 09:17:36 -0000
A Tutorial on Support Vector Machines for Pattern Recognition http://bit.ly/5jHlS3
Support Vector Machines Explained http://bit.ly/7Wo8NI
rstuven (Ricardo Stuven) Thu, 10 Dec 2009 09:17:36 -0000
Support Vector Machines Explained http://bit.ly/7Wo8NI

 
Subscribe to Support_Vector_Machines RSS feed

directory of related sites

artificial intelligence using SVM - Helpful to beginners trying to grasp the concepts of SVMs.

Image, Speech and Intelligent Systems Research Group - University of Southampton. Overview and links to resources.

Kernel Machines - A central source of information on kernel based methods, including support vector machines, Gaussian processes.

Lagrangian Support Vector Machine - University of Wisconsin at Madison. Software and technical report.
Meta Description: [ Active Support Vector Machine Home page ]

Learning to Classify Text using Support Vector Machines - By Thorsten Joachims - describes an SVM approach to text classification.

Support Vector Machine Mailing List - An unmoderated discussion list about Support Vector Machines methodology.

SVM Application List - Overview of domains in which SVMs have been applied.

Support_Vector_Machines related videos

Guys in the lab

This is the team of nerds of the Support Vector Machine project at CSUN. We're 4 grad students and an ugrad. It's good times

Support_Vector_Machines related videos

 

HOMEADVERTISINGABOUT US

articlesartsbusinesscomputersgameshealthhospitalshomekids & teensnewsmobilephysiciansrecreationreferenceregionalscienceshoppingsocietysportsworld


Submit a Site About Become an Editor