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Support Vector Machines

  • Ke-Lin Du
  • M. N. S. Swamy
Chapter

Abstract

SVM [12, 201] is one of the most popular nonparametric classification algorithms. It is optimal and is based on computational learning theory [200, 202]. The goal of SVM is to minimize the VC dimension by finding the optimal hyperplane between classes, with the maximal margin, where the margin is defined as the distance of the closest point in each class to the separating hyperplane. It has a general-purpose linear learning algorithm and a problem-specific kernel that computes the inner product of input data points in a feature space. The key idea of SVM is to project the training set in a high-dimensional space into a lower-dimensional feature space by means of a set of nonlinear kernel functions, where the projections of the training examples are always linearly separable in the feature space. The hippocampus, a brain region critical for learning and memory processes, has been reported to possess pattern separation function similar to SVM [6].

Keywords

Support Vector Support Vector Regression Quadratic Programming Problem Relevance Vector Machine Support Vector Regression Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Enjoyor LabsEnjoyor Inc.HangzhouChina
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

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