Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TLDR
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.Abstract:
The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.read more
Citations
More filters
Journal ArticleDOI
A review of unsupervised feature selection methods
TL;DR: A comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature is provided and a taxonomy of these methods is presented.
Journal ArticleDOI
Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
TL;DR: It is shown that exact leave-one-out cross-validation of sparse Least-Squares Support Vector Machines (LS-SVMs) can be implemented with a computational complexity of only O(ln2) floating point operations, rather than the O(l2n2) operations of a naïve implementation.
Proceedings ArticleDOI
Use of support vector learning for chunk identification
Taku Kudoh,Yuji Matsumoto +1 more
TL;DR: This paper investigates how SVMs with a very large number of features perform with the classification task of chunk labelling, CoNLL-2000 shared task, chunk identification.
Support Vector Machine Solvers
TL;DR: This chapter contains sections titled: Introduction, Support Vector Machines, Duality, Sparsity, Early SVM Algorithms, The Decomposition Method, A Case Study: LIBSVM, Conclusion and Outlook.
Journal ArticleDOI
Support vector machines for face recognition
TL;DR: The performance of the SVMs based face recognition is compared with the standard eigenface approach, and also the more recently proposed algorithm called the nearest feature line (NFL).
References
More filters
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
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.
Book
Methods of Mathematical Physics
Richard Courant,David Hilbert +1 more
TL;DR: In this paper, the authors present an algebraic extension of LINEAR TRANSFORMATIONS and QUADRATIC FORMS, and apply it to EIGEN-VARIATIONS.