Hitachi Vantara Pentaho Community Forums
Results 1 to 2 of 2

Thread: Looking for the right Hyperplane Algorithm

  1. #1
    Join Date
    Feb 2014
    Posts
    1

    Default Looking for the right Hyperplane Algorithm

    Hello,

    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
    Peter

  2. #2
    Join Date
    Aug 2006
    Posts
    1,741

    Default

    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.

    Cheers,
    Mark.

Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts
  •  
Privacy Policy | Legal Notices | Safe Harbor Privacy Policy

Copyright © 2005 - 2019 Hitachi Vantara Corporation. All Rights Reserved.