Hi everybody. I have a question about the implementation of SGD. As far as I can tell, it follows Leon Bottou's work while using the learning rate from Pegasos. As far as I can tell, a difference between Bottou's SGD and Shwartz's Pegasos is the projection step in Pegasos that enforces the regularization constrains (if I understood correctly). The authors claim that this is an important part of their algorithm.
What was the reason to favour the version of the algorithm without the projection step? Has anyone done any experiments on comparing the different SGD approaches? I am trying to get into this a bit more and would love to understand the differences. On a related topic: Has any one any experience in using SGD for kernelized SVMs? There is the LASVM by Bottou and Pegasos can also do kernelized classification. Would it be worth including this in sklearn? Cheers, Andy ------------------------------------------------------------------------------ The demand for IT networking professionals continues to grow, and the demand for specialized networking skills is growing even more rapidly. Take a complimentary Learning@Cisco Self-Assessment and learn about Cisco certifications, training, and career opportunities. http://p.sf.net/sfu/cisco-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
