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 proposal
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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
- Map Reduce: Distributed Computing for Machine Learning
Papers, videos and books related to machine learning in general:
- Collection of links to presentations on learning algorithms
- Programming Collective Intelligence
- Collective Intelligence in Action
- Data Mining: Practical Machine Learning Tools and Techniques
- Taming Text
- Machine Learning
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Introduction to Information Retrieval
All algorithms are either marked as integrated, that is the implementation is integrated into the development version of Mahout. Algorithms that are currently being developed are annotated with a link to the JIRA issue that deals with the specific implementation. Usually these issues already contain patches that are more or less major, depending on how much work was spent on the issue so far. Algorithms that have so far not been touched are marked as open.
What, When, Where, Why (but not How or Who) - Community tips, tricks, etc. for when to use which algorithm in what situations, what to watch out for in terms of errors. That is, practical advice on using Mahout for your problems.
Classification
A general introduction to the most common text classification algorithms can be found at Google Answers: http://answers.google.com/answers/main?cmd=threadview&id=225316 For information on the algorithms implemented in Mahout (or scheduled for implementation) please visit the following pages.
Logistic Regression (open)
Complementary Naive Bayes (MAHOUT-60)
Support Vector Machines (SVM) (open: MAHOUT-14)
Neural Network (open)
Clustering
Canopy Clustering (integrated)
k-Means (integrated)
Expectation Maximization (EM) (MAHOUT-28)
Hierarchical Clustering (MAHOUT-19)
Dirichlet Process Clustering (MAHOUT-30)
Regression
Locally Weighted Linear Regression (open)
Dimension reduction
Principal Components Analysis (PCA) (open)
Independent Component Analysis (open)
Gaussian Discriminative Analysis (GDA) (open)
Evolutionary Algorithms
see also: MAHOUT-56
You will find here information, examples, use cases, etc. related to Evolutionary Algorithms.
Introductions and Tutorials:
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) (open)
Recommendation Learning (integrated)
Data
Historical Information
Project inspiration and formulation can be found at http://ml-site.grantingersoll.com
Committer's Resources
Apache Machine Status – Check to see if SVN, other resources are available