I've just learned about mlpy which is open source python machine learning software. It includes a good variety of machine learning algorithms including SVM, k-NN, LASSO, ridge regression, k-mean clustering, k-median clustering, wavlet transforms, and resampling methods. It uses numpy as one would expect. But, I see no mention of the use of sparse matrices, so I suspect that the library would not scale well to large feature matrices, which is typically essential in my work.

It looks like a good resource for open source ML software is MLoss, which organized a NIPS workshop.

I would suggest you look at scikit-learn, it both provides support for sparse matrices as well as starting to make some inroads into online learning (there is a SGD algorithm for binary classification already in) and development is very active.

ReplyDeleteCool. Besides sparsity, SGD is something I'd like to use more of.

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