Least Squares Support Vector Machines : an Overview

@inproceedings{Suykens2002LeastSS,
  title={Least Squares Support Vector Machines : an Overview},
  author={Johan Suykens},
  year={2002}
}
Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led recently to many new developments in kernel based learning in general. In these methods one solves convex optimization problems, typically quadratic programs. We focus on Least Squares Support Vector Machines which are reformulations to standard SVMs that lead to solving linear KKT systems. Least squares support vector machines are… CONTINUE READING
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