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Alex Rivera
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I am using libSVM. Say my feature values are in the following format: instance1 : f11, f12, f13, f14 instance2 : f21, f22, f23, f24 instance3 : f31, f32, f33, f34 instance4 : f41, f42, f43, f44 .............................. instanceN : fN1, fN2, fN3, fN4 I think there are two scaling can be applied. scale each instance vector such that each vector has zero mean and unit variance. ( (f11, f12, f13, f14) - mean((f11, f12, f13, f14) ). /std((f11, f12, f13, f14) ) scale each colum of the above matrix to a range. for example [-1, 1] According to my experiments with RBF kernel (libSVM) I found that the second scaling (2) improves the results by about 10%. I did not understand the reason why (2) gives me a improved results. Could anybody explain me what is the reason for applying scaling and why the second option gives me improved results?
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