Could any body explain that " how does linear svm work for prediction in the classification process?"
Many thanks,
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http://en.wikipedia.org/wiki/Support_vector_machine – Marc Claesen Mar 04 '14 at 14:36
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Very briefly, support vector machines learn a linearly separating hyperplane that best divides two classes of data within their collective feature space. There are many good tutorials that will expand upon this! Here is one.
Kyle.
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Thank you, but I meant how does svm decide that this test point belongs to this class. – Weam Mar 04 '14 at 16:17
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@wearn I see. The tutorial should cover that. Test points are projected into the feature space defined by your training data, and their class is assigned based on which side of the decision boundary they lie. – Kyle. Mar 04 '14 at 16:22