Parameters for Support Vector Regression using LibSVM in Matlab

Question

I am trying to use LibSVM for regression. I am trying to detect faces (10 classes of different faces). I labeled 1-10 as face class and 11 is for non face. I want to develop a script usig LibSVM which will give me a continuous score between 0-1 if the test image falls to any of the 10 face class, otherwise it will give me -1 (non-face). From this score, I can be able to predict my calss. If the test image is matched with 1st class, the score should be around .1. Similarly if the test image is matched with class 10, the score should be around 1 (any continuous value close to 1). I am trying to use SVR using LibSVM to solve this problem. I can easily get the predicted class through classification. But I want a continuous score value which I can get through regression. Now, I was looking in the net for the function or parameters in function for SVR using LibSVM, but I couldn't find anything. Can anybody please help me in this regard?

Answer 1

This is not a regression problem. Solving it through regression will not yield good results.

You are dealing with a multiclass classification problem. The best way to approach this is to construct 10 one-vs-all classifiers with probabilistic output. To get probabilistic output (eg in the interval [0,1]), you can train and predict with the -b 1 option for C-SVC ( -s 0 ).

If any of the 10 classifiers yields a sufficiently large probability for its positive class, you use that probability (which is close to 1). If none of the 10 classifiers yield a positive label with high enough confidence you can default to -1.

So in short: make a multiclass classifier containing one-vs-all classifiers with probabilistic output. Subsequently post-process the predictions as I described, using a probability threshold of your choice (for example 0.7).