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Logo SenseClusters 1.01

by tpederse - August 12, 2008, 16:39:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6309 views, 1831 downloads, 1 subscription

About: SenseClusters is a package of (mostly) Perl programs that allows a user to cluster similar contexts together using unsupervised knowledge-lean methods. These techniques have been applied to word [...]

Changes:

Initial Announcement on mloss.org.


Logo OLL 0.02

by hillbig - May 21, 2009, 10:08:31 CET [ Project Homepage BibTeX Download ] 6287 views, 1377 downloads, 1 subscription

About: OLL is a library supporting several for online-learning algorithms, which provides C++ library, and stand-alone programs for learning, predicting. OLL is specialized for large-scale, but sparse, [...]

Changes:

Initial Announcement on mloss.org.


Logo K tree 0.4.2

by cdevries - July 4, 2011, 06:01:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6282 views, 1392 downloads, 1 subscription

About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms.

Changes:

Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.


Logo r-cran-gbm 2.0-8

by r-cran-robot - January 17, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 6244 views, 1218 downloads, 1 subscription

About: Generalized Boosted Regression Models

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.019963


About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Changes:

Initial Announcement on mloss.org.


Logo multi assignment clustering of Boolean data 2.001

by mafrank - March 3, 2012, 09:04:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6239 views, 838 downloads, 1 subscription

About: Implementation of the multi-assignment clustering method for Boolean vectors.

Changes:

new bib added


Logo JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6218 views, 2230 downloads, 2 subscriptions

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

Changes:

The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:

  1. Support for multithreading in training and prediction with ensemble models. Since both of these are embarassingly parallel, this has induced a significant speedup (3-fold on quad-core).
  2. Extensive programming framework for aggregation of base model predictions which allows highly efficient prototyping of new aggregation approaches. Additionally we provide several predefined strategies, including (weighted) majority voting, logistic regression and nonlinear SVMs of your choice -- be sure to check out the esvm-edit tool! The provided framework also allows you to efficiently program your own, novel aggregation schemes.
  3. Full code transition to C++11, the latest C++ standard, which enabled various performance improvements. The new release requires moderately recent compilers, such as gcc 4.7.2+ or clang 3.2+.
  4. Generic implementations of convenient facilities have been added, such as thread pools, deserialization factories and more.

The API and ABI have undergone significant changes, many of which are due to the transition to C++11.


Logo Vowpal Wabbit 2.3

by JohnLangford - December 21, 2007, 20:43:40 CET [ Project Homepage BibTeX Download ] 6207 views, 1064 downloads, 0 subscriptions

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About: This is a large scale online learning implementation with several useful features. See the webpage for more details.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-rattle 2.6.26

by r-cran-robot - March 16, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 6167 views, 1349 downloads, 0 subscriptions

About: Graphical user interface for data mining in R

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:07.700426


Logo OpenCV, The Open Computer Vision Library 1.0

by bornet - April 24, 2008, 12:15:59 CET [ Project Homepage BibTeX Download ] 6142 views, 1664 downloads, 0 comments, 1 subscription

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(based on 3 votes)

About: The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL and utilizes Intel Integrated Performance [...]

Changes:

Initial Announcement on mloss.org.


Showing Items 181-190 of 561 on page 19 of 57: First Previous 14 15 16 17 18 19 20 21 22 23 24 Next Last