[ https://issues.apache.org/jira/browse/SYSTEMML-1938?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Janardhan updated SYSTEMML-1938: -------------------------------- 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 -- This message was sent by Atlassian JIRA (v6.4.14#64029)