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Support-Vector Networks

Support-Vector Networks 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Learning Springer Journals

Support-Vector Networks

Machine Learning , Volume 20 (3) – Aug 26, 2004

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References (28)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Control, Robotics, Mechatronics; Artificial Intelligence; Simulation and Modeling; Natural Language Processing (NLP)
ISSN
0885-6125
eISSN
1573-0565
DOI
10.1023/A:1022627411411
Publisher site
See Article on Publisher Site

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.

Journal

Machine LearningSpringer Journals

Published: Aug 26, 2004

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