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https://issues.apache.org/jira/browse/SYSTEMML-1938?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Janardhan updated SYSTEMML-1938:
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Description:
RGF is a machine learning method for building decision forests. Based on
1. Learning Nonlinear Functions UsingRegularized Greedy Forest -
https://arxiv.org/pdf/1109.0887.pdf
2. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost -
https://arxiv.org/ftp/arxiv/papers/1203/1203.3491.pdf
3. A general boosting method and its application to learning ranking functions
for web search -
https://papers.nips.cc/paper/3305-a-general-boosting-method-and-its-application-to-learning-ranking-functions-for-web-search.pdf
A C++ implementation is at https://github.com/baidu/fast_rgf
was:
RGF is a machine learning method for building decision forests. Based on
Learning Nonlinear Functions Using
Regularized Greedy Forest https://arxiv.org/pdf/1109.0887.pdf
A C++ implementation is at https://github.com/baidu/fast_rgf
> Regularized Greedy Forest (RGF) Implementation
> ----------------------------------------------
>
> Key: SYSTEMML-1938
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1938
> Project: SystemML
> Issue Type: New Feature
> Components: Algorithms
> Reporter: Janardhan
> Priority: Minor
>
> RGF is a machine learning method for building decision forests. Based on
> 1. Learning Nonlinear Functions UsingRegularized Greedy Forest -
> https://arxiv.org/pdf/1109.0887.pdf
> 2. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost -
> https://arxiv.org/ftp/arxiv/papers/1203/1203.3491.pdf
> 3. A general boosting method and its application to learning ranking
> functions for web search -
> https://papers.nips.cc/paper/3305-a-general-boosting-method-and-its-application-to-learning-ranking-functions-for-web-search.pdf
> A C++ implementation is at https://github.com/baidu/fast_rgf
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