Book Chapter10.1007/978-3-030-26622-6_7
Support Vector Machines
Ameet V Joshi
- 01 Jan 2020
- pp 65-71
9
TL;DR: The concept of support vector machines as developed by Vapnik and others is studied to see how it can be further generalized for nonlinear problems with use of kernels and also how it is extended for solving the problems of regression.
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Abstract: In this chapter we are going to study the concept of support vector machines as developed by Vapnik and others. This concept was first proposed as an alternative to neural networks, when neural networks were not performing up to the grand expectations that they came with. SVM proposed a very targeted mathematical approach towards finding the optimal solution in case of classification or regression. We will first study the original SVM theory that tries to solve the problem of linear classification. Then we will see how it can be further generalized for nonlinear problems with use of kernels and also how it is extended for solving the problems of regression. Theory of SVM proposed an elegant solution towards optimization and generalization and more importantly was extremely successful in getting results that neural network based methods only hoped for at the time.
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References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
A training algorithm for optimal margin classifiers
Bernhard E. Boser,Isabelle Guyon,Vladimir Vapnik +2 more
- 01 Jul 1992
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.