In every book and example always they show only binary classification (two classes) and new vector can belong to any one class.
Here the problem is I have 4 classes(c1, c2, c3, c4). I've training data for 4 classes.
For new vector the output should be like
C1 80% (the winner)
c2 10%
c3 6%
c4 4%
How to do this? I'm planning to use libsvm (because it most popular). I don't know much about it. If any of you guys used it previously please tell me specific commands I'm supposed to use.
LibSVM uses the one-against-one approach for multi-class learning problems. From the FAQ:
Q: What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method ?
It is one-against-one. We chose it after doing the following comparison: C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines, IEEE Transactions on Neural Networks, 13(2002), 415-425.
"1-against-the rest" is a good method whose performance is comparable to "1-against-1." We do the latter simply because its training time is shorter.