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Subject [CONF] Apache Lucene Mahout: index (page edited)
Date Mon, 09 Jun 2008 10:10:03 GMT
index (MAHOUT) edited by abdelhakim deneche


h1. Apache Mahout Wiki

Apache Mahout is a new Lucene TLP project to create scalable, machine learning algorithms
under the Apache license. For more information on the project goals please see the [original


h2. Historical Information

Project inspiration and formulation can be found at []

h2. General






h2. Design


[Matrix and Vector Needs]

h2. Algorithms

This section contains links to information, examples, use cases, etc. for the various algorithms
we intend to implement.  Click the individual links to learn more. The initial algorithms
descriptions have been copied here from the original project proposal. The algorithms are
grouped by the application setting, they can be used for. In case of multiple applications,
the version presented in the paper was chosen, versions as implemented in our project will
be added as soon as we are working on them.

Original Paper: [Map Reduce for Machine Learning on Multicore|]

Papers related to Map Reduce:
   * [Evaluating MapReduce for Multi-core and Multiprocessor Systems|]

h3. Classification

A general introduction to the most common text classification algorithms can be found at Google
Answers: For information
on the algorithms implemented in Mahout (or scheduled for implementation) please visit the
following pages.

[Logistic Regression]


[Support Vector Machines] (SVM)

[Neural Network]

h3. Clustering

[Canopy Clustering]


[Expectation Maximization] (EM)

[Mean Shift]

h3. Regression

[Locally Weighted Linear Regression]

h3. Dimension reduction

[Principal Components Analysis ] (PCA)

[Independent Component Analysis]

[Gaussian Discriminative Analysis] (GDA)

h3. Non map reduce algorithms

Some algorithms and applications appeared on the mailing list, that have not been published
in map reduce form so far. As we do not restrict ourselves to hadoop-only versions, these
proposals are listed here.

[Hidden Markov Models] (HMM)

[Recommendation Learning]

h3. Evolutionary Algorithms

You will find here information, examples, use cases, etc. related to Evolutionary Algorithms.

Introductions and Tutorials:
   * [Evolutionary Algorithms Tutorials|]

h2. Data


h2. Community


h2. Committer's Resources




[Apache Machine Status|] -- Check to see if SVN, other
resources are available

h3. Other Resources

[Committer's FAQ|]

[Apache Dev|]

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