Machine Learning frameworks, libraries and software
github.com
github.com
glmnet - lasso/ridge/elastic net glm models.
e1071 - SVM classifiers.
randomForest - random forest classifiers.
mixOmics - a good collection of component-based approaches (PCA, ICA, PLS, etc. includes sparse variants of all of the above is feature selection is required).
caret - similar to Java's Weka.
- TMVA (Toolkit for Multivariate Analysis): Widely used in physics, esp. particle physics. Has every classifier you can think of and the kitchen sink, neural nets, BDTs, support vector machines, fisher discriminants, etc.. You can use it for parameter estimation, classification, discrimination and other use cases. Is closely integrated with the ROOT framework, which has a few quirks and gives it a bit of a learning curve, but once you get into it it's very easy to make a multivariate analysis. Also has bindings for Python. - http://tmva.sourceforge.net/
- NeuroBayes: Heard some good things about it, but havent tested it. Used in finance and particle physics. Commercial, but they have special licenses for research. I heard integration with TMVA is planned. - http://neurobayes.phi-t.de/index.php/public-information
In my case, any library licensed under the GPL is automatically excluded from consideration, so this is a significant factor. I'd rather not spend any time on those.
So, in practical terms, it depends on who my current client/employer/investor is. Myself, I'd rather not use any LGPLd libraries.
My comment was precise, informative, in reply to a question that was asked of me, based on a number of legal opinions and more years of experience than many people here write into the "age" field on forms.
And sort languages alphabetically, please.
Also, I thought you would add all Machine Learning libs, not just link to the Clojure Toolbox.