Options
regarding the kernel weight output of libsvm operator
huaiyanggongzi
Member Posts: 39 Contributor II
I applied libsvm operator for several data sets, and found that the kernel weight values of the built model tend to be always positive. For instance, IĀ can have
However, according to SVM theory, the weight vector should satisfy equation of
i 1.9108829072778841 cp 1.762460806463015 medimmune 1.630318586802012 |
wx+b =0The x is the points located on the decision hyperplane. The entries in the weight vector cannot always be larger than zero. Does the weight vector output by Rapidminer has a different physical meaning than the SVM theory?
0
Answers
E.g. w=(1,1,1), b = 0 specifies a plane where x1 + x2 + x3 = 0, which is perfectly realizable.
Best regards,
Marius
With respect to the data set, the feature vectors are constructed using binary occurrence for text files, which cannot have any negative x values. However, all of the weight values are still bigger than zero. I tried several data sets and observed the same scenario. Thanks.
But despite of the weight representation, the libSVM obviously does quite a good job. However, if you need signed weights, you should try the Support Vector Machine operator (without any additions to the name).
Best regards,
Marius