Here's some sources of data which could be used with learning or data mining algorithms.

If you want to do writing job real statistically valid comparisons, you need to be aware of potential problems. For a good introduction to these, see the paper "On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach" ( http://www.cs.jhu.edu/~salzberg/critique.ps ) by Steven Salzberg? ( http://www.cs.jhu.edu/~salzberg/ )

* MLnet Machine Learning Archive Datasets
* The UCIRepository Of Machine Learning Databases And Domain Theories
* Stat Lib Datasets Archive
* University Of Toronto SDelve Datasets
* Statlog Datasets
* Kdd Sisyphus
* Synthetic Classification Data Sets


-- Andy Pryke? - 21 Oct 2001

See also:

Here's some sources of data which could be used with learning or data mining algorithms.

If you want to do real statistically valid comparisons, you need to be aware of potential problems. For a good introduction to these, see the paper "On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach" ( http://www.cs.jhu.edu/~salzberg/critique.ps ) by Steven Salzberg? ( http://www.cs.jhu.edu/~salzberg/ )

* MLnet Machine Learning Archive Datasets
* The UCIRepository Of Machine Learning Databases And Domain Theories
* Stat Lib Datasets Archive
* University Of Toronto SDelve Datasets
* Statlog Datasets
* Kdd Sisyphus
* Synthetic Classification Data Sets

-- Andy Pryke? - 21 Oct 2001

See also:

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Topic revision: r34 - 03 Sep 2010 - 16:05:42 - Liam Martone

 
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