"SVM label attribute (positive or Negative examples)"

Question

Hi,

I need to clarify, whether the attribute indicating the positive and negative examples (label attribute: 1,-1) of an SVM exampleset is an input to SVM or is it the output of SVM. I am a bit confused on this.

To put my question in another way,

Suppose i want to perform a HyperHyper SVM for the exampleset in, .....rm_workspace\sample\data/polynomial.dat. I cannot run the HyperHyper SVM on this because, RM ask for the label/class attribute. But, how do i create this attribute for the data, polynomial.dat? On what basis should I put 1 or -1 to the examples of the dataset? (I thought SVM will give +1/-1 column as an output)

Sorry if I did not get the topic well, i am a bit confused with this...

Thanks for your help,
Shubha

Shubha · Answer

I sincerely apologize if this thread caused any problems or waste of time...

BR, Shubha

haddock · Answer

With respect, I feel you should work through the examples and stop posting until you have. Why? Because you are getting the most basic things wrong. Surely you noticed that SVMs are supervised learners, and that the cluster operators are unsupervised. But you did not understand even that fundamental difference  ::)

A quick Google on "Shubha Karanth" indicates that you, or your named double, work for computer consultancies, should we send them the bill for our time? Or would it be better for you to read the manuals and do the exercises?

Shubha · Answer

Thanks for your reply Steffen.

Yes, I have started the SVM technique two days back. I was reading the SVM links, all say to have a label attribute, the C class. (I tried with R software, which also did the same by taking a vector with binaries/categories). And Rapid Miner too asked for the label attribute.

But since I had done some 'cluster analysis' before (again a classification technique), which does not take any label attributes, but creates the category(label) as the output, I expected the same here in SVM too, which is again a classification technique (any comments here?). So, wanted to clear my confusion.

So in a SVM, one already knows which category does the vector belongs to in a training set and hence will learn from the model and can apply the learning on any test data(which doesn't have the label) and creates label for the test data. Is this correct?

(One more stupid doubt is: Creating a binomial label attribute is based on some theory right? Like, Certain vectors belong to a Red box and rest belongs to a Green box. It is not just the random flow of binary values.)..

Thanks for clearing the issues I have.

Shubha