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Theory, Implementation, and Applications of Support Vector Machines

  • Massimiliano Pittore
  • Alessandro Verri
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

Support Vector Machines (SVMs) have been recently introduced as a new method for function estimation. In this survey we first review the main theoretical properties of SVMs, then present an implementation of SVMs able to work with training sets of very large size. Finally, we discuss two computer vision applications in which SVMs for both pattern recognition and regression estimation have been successfully employed.

Keywords

Support Vector Machine Support Vector Regression Estimation Structural Risk Minimi Computer Vision Application 
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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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Massimiliano Pittore
    • 1
  • Alessandro Verri
    • 2
    • 3
  1. 1.INFM — DISIUniversità di GenovaGenovaItaly
  2. 2.INFM — DISIUniversità di GenovaGenovaItaly
  3. 3.Center for Biological and Computational LearningMITCambridgeUSA

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