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Logo monte python 0.1.0

by roro - May 9, 2008, 21:45:47 CET [ Project Homepage BibTeX Download ] 5533 views, 2169 downloads, 1 subscription

About: Monte (python) is a small machine learning library written in pure Python. The focus is on gradient based learning, in particular on the construction of complex models from many smaller components.

Changes:

Initial Announcement on mloss.org.


Logo mloss.org svn-r645-Mar-2011

by sonne - March 23, 2011, 11:09:18 CET [ Project Homepage BibTeX Download ] 15000 views, 2082 downloads, 1 subscription

About: This is the source code of the mloss.org website.

Changes:

Now works with newer django versions and fixes several warnings and minor bugs underneath. The only user visible change is probably that the subscription and bookmark buttons work again.


Logo Debellor 1.0

by mwojnars - July 30, 2009, 16:48:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7198 views, 2082 downloads, 1 subscription

About: Debellor is a scalable and extensible platform which provides common architecture for data mining and machine learning algorithms of various types.

Changes:
  • Naming of numerous classes/methods/fields changed to be more accurate and comprehensible
  • Weka and Rseslib libraries updated to the newest versions: Weka 3.6.1 & Rseslib 3.0.1. Debellor's wrappers adapted
  • New class: CrossValidation - evaluator of trainable cells through cross-validation
  • New class: RMSE - calculation of Root Mean Squared Error score
  • Data objects can be compared and used in collections
  • ArffReader can read from a user-provided java.io.InputStream
  • More convenient use of parameters (setting values)
  • More convenient use of data objects and data types (construction, type casting)
  • Other minor improvements to existing classes
  • Javadoc extended

Logo TMBP 1.0

by zengjia - April 5, 2012, 06:42:26 CET [ BibTeX BibTeX for corresponding Paper Download ] 4225 views, 2075 downloads, 2 subscriptions

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About: Message passing for topic modeling

Changes:
  1. improve "readme.pdf".
  2. correct some compilation errors.

Logo r-cran-svmpath 0.952

by r-cran-robot - February 1, 2012, 00:00:11 CET [ Project Homepage BibTeX Download ] 9932 views, 2074 downloads, 1 subscription

About: svmpath

Changes:

Fetched by r-cran-robot on 2012-02-01 00:00:11.755984


Logo OpenCog pre-1.0

by ferrouswheel - January 11, 2009, 22:51:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4536 views, 2067 downloads, 1 subscription

About: OpenCog aims to provide research scientists and software developers with a common platform to build and share artificial intelligence programs. The long-term goal of OpenCog is acceleration of the [...]

Changes:

Initial Announcement on mloss.org.


Logo kernlab 0.9-9

by alexis - November 2, 2009, 16:03:50 CET [ Project Homepage BibTeX Download ] 10059 views, 2066 downloads, 0 subscriptions

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About: kernlab provides kernel-based Machine Learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab [...]

Changes:

minor fixes in kcca and ksvm functions


Logo Aleph 0.6

by jiria - January 12, 2009, 20:52:12 CET [ Project Homepage BibTeX Download ] 7080 views, 2063 downloads, 1 subscription

About: Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.

Changes:

Initial Announcement on mloss.org.


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 chi2 kernel 1.5

by gruel - February 15, 2009, 22:32:21 CET [ BibTeX Download ] 8960 views, 1996 downloads, 1 subscription

About: Very fast implementation of the chi-squared distance between histograms (or vectors with non-negative entries).

Changes:

Removed bug in symmetric chi-square distance and updated python wrapper to python 2.5 compatiblity.


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