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What would be the best approach to choosing the multi-class strategy (MSVM, OVA, ECOC) for classifying a large number of classes with limited examples of each class?

Another two factors that define the solution, in my case:

  • The probability of prediction is an important factor that decides the next steps in my solution.
  • The size (i.e. size of the classifier object on disk which can later be loaded in memory and used for prediction), of the fitted classifier (since I need the solution to be portable without providing it as a service). I do not need to analyze the classifier(s) any more than getting the category with reasonable probability.
    UPDATE: The problem is basically that of text classification.
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Would you elaborate more on what you mean by the "size" of the classifier i.e. disk storage or complexity or what ? – soufanom Feb 26 '13 at 4:33
Size as in disk of now I am trying to get the answers without limiting the features and accuracy. – Ark Mar 2 '13 at 6:23

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