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Add 'Support-Vector Networks' paper.
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* [A Few Useful Things to Know about Machine Learning](http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf) - Just like the title says, it contains many useful tips and gotchas for machine learning
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* [Random Forests](http://oz.berkeley.edu/~breiman/randomforest2001.pdf) - The initial paper on random forests
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* [Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data](http://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers) - The paper introducing conditional random fields as a framework for building probablistic models.
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* [Support-Vector Networks](http://rd.springer.com/content/pdf/10.1007%2FBF00994018.pdf) - The initial paper on support-vector networks for classification.
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