Q:

Is TF-IDF necessary when using SVM?

I'm using Support Vector Machines to classify phrases. Before using the SVM, I understand I should do some kind of normalization on the phrase-vectors. One popular method is TF-IDF.
The terms with the highest TF-IDF score are often the terms that best characterize the topic of the document.
But isn't that exactly what SVM does anyway? Giving the highest weight to the terms that best characterize the document?
Thanks in advance :-)

A:

The weight of a term (as assigned by an SVM classifier) may or may not be directly proportional to the relevance of that term to a particular class. This depends on the kernel of the classifier as well as the regularization used. SVM does NOT assign weights to terms that best characterize a single document.
Term-frequency (tf) and inverse document frequency (idf) are used to encode the value of a term in a document vector. This is independent of the SVM classifier. 

