Projects that are tagged with boosting.


Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 2806 views, 854 downloads, 3 subscriptions

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

Changes:
  • added ElasticNets as a regularization option
  • fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
  • added a couple of I/O optimizations

Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11423 views, 4478 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23436 views, 4121 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


Logo Cubist 2.07

by zenog - September 2, 2011, 14:52:17 CET [ Project Homepage BibTeX Download ] 2330 views, 609 downloads, 1 subscription

About: Cubist is the regression counterpart to the C5.0 decision tree tool.

Changes:

Initial Announcement on mloss.org.


Logo C5.0 2.07

by zenog - September 2, 2011, 14:49:04 CET [ Project Homepage BibTeX Download ] 2539 views, 640 downloads, 1 subscription

About: C5.0 is the successor of the C4.5 decision tree algorithm/tool. In particular, it is faster and more memory-efficient.

Changes:

Initial Announcement on mloss.org.


Logo Graph Learning Package 0.1

by hiroto - May 4, 2009, 17:07:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6778 views, 1280 downloads, 0 subscriptions

About: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features.

Changes:

Initial Announcement on mloss.org.


Logo MinorThird 20080414

by frank - June 9, 2008, 09:08:30 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6243 views, 1809 downloads, 1 subscription

About: MinorThird is a collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text. It was written primarily by William W. Cohen, a professor at [...]

Changes:

Initial Announcement on mloss.org.


Logo boostree 0.1

by xavierc - December 1, 2007, 03:16:14 CET [ BibTeX Download ] 3898 views, 1352 downloads, 0 comments, 0 subscriptions

About: This package provides an implementation Schapire and Singer's AdaBoost.MH for multi-label classification. As a main feature, the package provides decision-tree weak learning, a generalization of [...]

Changes:

Initial Announcement on mloss.org.


Logo iBoost 0.1

by hiroto - December 1, 2007, 00:34:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4528 views, 1125 downloads, 0 subscriptions

About: Itemset boosting (iBoost) performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation [...]

Changes:

Initial Announcement on mloss.org.