I want to work with machine learning in Mathematica. Are there any SVM algorithms implemented in Mathematica anywhere? Or any other algorithms for machine learning? With positive and negative database of HOG descriptors.

  • $\begingroup$ May you could i) give us a reference ii) give it a shot? $\endgroup$
    – chris
    Nov 21, 2012 at 14:01
  • $\begingroup$ There's this wolfram.com/products/applications/neuralnetworks 35GBP for students. Haven't used it myself I usually export the data and handle it in matlab since I have the neural networks package for matlab. It makes me cry every time EDIT: Can't find anything about SVM in the Mathematica neural networks package, perhaps it doesn't even have it $\endgroup$
    – ssch
    Nov 21, 2012 at 14:43
  • $\begingroup$ What do you mean by "positive and negative database"? $\endgroup$ Nov 21, 2012 at 14:44
  • $\begingroup$ Are you testing HOG fetures on SVM method? what's your dataset, how about the real performance of Mathematica's implementation. $\endgroup$ Dec 29, 2022 at 18:56

2 Answers 2


As of Version 10 , Mathematica has a built in function Classify, which implements support vector machines and some other common machine learning algorithms.

trainingset = {1 -> "A", 2 -> "A", 3.5 -> "B", 4 -> "B"};
classifier  = Classify[ trainingset, Method -> "SupportVectorMachine"];

The Mathematica Journal has a nice article on SVM's: A Flexible Implementation for Support Vector Machines, with an accompanying notebook and .m file providing an SVM implementation.

  • $\begingroup$ Calls to the defunct RandomArray can be replaced with identical ones to RandomReal in the example SVM notebook. $\endgroup$ Nov 21, 2012 at 14:59
  • 2
    $\begingroup$ There's a (newer?) version of the package on google code: code.google.com/p/prpackage/source/browse/t/draft/classify/… $\endgroup$ Nov 21, 2012 at 15:01
  • $\begingroup$ Would it be too much to ask for a break down of this code? $\endgroup$
    – SumNeuron
    Dec 5, 2017 at 11:46
  • $\begingroup$ The link seems lost, do you know where to download a pdf version? $\endgroup$ Dec 18, 2022 at 15:36
  • $\begingroup$ @HyperGroups The web archive has a couple of snapshots of this page. I also found the PDF here. But why would you want it? SVMs are already a built-in method of Classify. $\endgroup$ Dec 19, 2022 at 22:09

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