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Updated Random forests paper location
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* [Top 10 algorithms in data mining](http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf) - While it is difficult to identify the top 10, this paper contains 10 very important data mining/machine learning algorithms * [Top 10 algorithms in data mining](http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf) - While it is difficult to identify the top 10, this paper contains 10 very important data mining/machine learning algorithms
* [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 * [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
* [Random Forests](http://oz.berkeley.edu/~breiman/randomforest2001.pdf) - The initial paper on random forests * [Random Forests](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) - The initial paper on random forests
* [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. * [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.
* [Support-Vector Networks](http://rd.springer.com/content/pdf/10.1007%2FBF00994018.pdf) - The initial paper on support-vector networks for classification. * [Support-Vector Networks](http://rd.springer.com/content/pdf/10.1007%2FBF00994018.pdf) - The initial paper on support-vector networks for classification.
* [The Fast Johnson-Lindenstrauss Transforms](https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf) * [The Fast Johnson-Lindenstrauss Transforms](https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf)