From e1b14ffac3825db3b528a8a89041119ddfc71e40 Mon Sep 17 00:00:00 2001 From: Visgean Skeloru Date: Sat, 5 Mar 2016 15:56:22 +0100 Subject: [PATCH] Updated Random forests paper location the old one was not responding and https://www.stat.berkeley.edu/~breiman/papers.html links to new location. --- machine_learning/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/machine_learning/README.md b/machine_learning/README.md index 2f55d2c..ed265fb 100644 --- a/machine_learning/README.md +++ b/machine_learning/README.md @@ -4,7 +4,7 @@ * [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 -* [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. * [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)