Bayes Point Machines

  title={Bayes Point Machines},
  author={Ralf Herbrich and Thore Graepel and Colin Campbell},
  journal={Journal of Machine Learning Research},
Kernel-classifiers comprise a powerful class of non-linear decision functions for binary classification. The support vector machine is an example of a learning algorithm for kernel classifiers that singles out the consistent classifier with the largest margin, i.e. minimal real-valued output on the training sample, within the set of consistent hypotheses, the so-called v rsion space. We suggest theBayes point machine as a well-founded improvement which approximates the Bayes-optimal decision by… CONTINUE READING
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