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
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Journal ArticleDOI
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
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BookDOI
Support Vector Machines: Theory and Applications
TL;DR: This chapter discusses Kernel Discriminant Learning with Application to Face Recognition, Fast Color Texture-based Object Detection in Images: Application to License Plate Localization, and more.
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Fast Marginal Likelihood Maximisation for Sparse Bayesian Models
Michael E. Tipping,A. C. Faul +1 more
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Implementation of machine-learning classification in remote sensing: an applied review
TL;DR: An overview of machine learning from an applied perspective focuses on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN).
References
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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.