Constant Rate Approximate Maximum Margin Algorithms

  • Petroula Tsampouka
  • John Shawe-Taylor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)


We present a new class of Perceptron-like algorithms with margin in which the “effective” learning rate η eff, defined as the ratio of the learning rate to the length of the weight vector, remains constant. We prove that for η eff sufficiently small the new algorithms converge in a finite number of steps and show that there exists a limit of the parameters involved in which convergence leads to classification with maximum margin. A soft margin extension for Perceptron-like large margin classifiers is also discussed.


Weight Vector Learning Rate Training Pattern Maximum Margin Extended Space 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Petroula Tsampouka
    • 1
  • John Shawe-Taylor
    • 1
  1. 1.ECSUniversity of SouthamptonUK

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