The only tricky part would be writing a quadratic solver. Alternatives: either solve a linear SVM using gradient descent (simpler to write), or offload the core of the algorithm to an existing solver like cvxopt.
On reflection I guess I might have had more free time to spend on this than a normal person - I did the SVM as a [small] part of my masters project, so if you're time constrained with a real job and a life then might be best to disregard me.
If you had the quadratic solver, I would think it would be reasonable to add the rest of the code. If you started adding costs, gammas, etc. I would think it would take a while. I spent hours looking at the source code of libSVM at my last job and never really understood what the hell was going on
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Implementing the SVM from scratch was time consuming - no?
The only tricky part would be writing a quadratic solver. Alternatives: either solve a linear SVM using gradient descent (simpler to write), or offload the core of the algorithm to an existing solver like cvxopt.
edit: For an example of using cvxopt, check out http://www.mblondel.org/journal/2010/09/19/support-vector-ma...
Another approach is to implement Platt's SMO:
http://en.wikipedia.org/wiki/Sequential_minimal_optimization
cool - thanks
Yes it was :) but time well spent imo.
On reflection I guess I might have had more free time to spend on this than a normal person - I did the SVM as a [small] part of my masters project, so if you're time constrained with a real job and a life then might be best to disregard me.
If you had the quadratic solver, I would think it would be reasonable to add the rest of the code. If you started adding costs, gammas, etc. I would think it would take a while. I spent hours looking at the source code of libSVM at my last job and never really understood what the hell was going on