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Thread: Looking for the right Hyperplane Algorithm

  1. #1
    Join Date
    Feb 2014

    Default Looking for the right Hyperplane Algorithm


    I have a two sets of speech instances with 39 floating point features. I believe these sets are not linearly separable.

    I want to find a hyperplane that does the "best job" possible of separating them. By "best job" I mean that on average the instances are best separated in distance from the plane. I do not care about classification performance.

    I am considering a simple Perceptron or SVM. How do these algorithms perform for data that is not separable? Is there some other algorithm I should try?

    Thank you

  2. #2
    Join Date
    Aug 2006


    Hi Peter,

    SVMs can be applied to the non-linear case by using non-linear kernels (such as RBF or high order polynomials). Since they find the maximum margin hyperplane, this should meet your "best separated on average criterion". There is some parameter tuning required however, so experimentation is required.


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