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Logo monte python 0.1.0

by roro - May 9, 2008, 21:45:47 CET [ Project Homepage BibTeX Download ] 5568 views, 2191 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 TMBP 1.0

by zengjia - April 5, 2012, 06:42:26 CET [ BibTeX BibTeX for corresponding Paper Download ] 4388 views, 2170 downloads, 2 subscriptions

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About: Message passing for topic modeling

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

Logo mloss.org svn-r645-Mar-2011

by sonne - March 23, 2011, 11:09:18 CET [ Project Homepage BibTeX Download ] 15160 views, 2109 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 ] 7291 views, 2105 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 r-cran-svmpath 0.952

by r-cran-robot - February 1, 2012, 00:00:11 CET [ Project Homepage BibTeX Download ] 10049 views, 2096 downloads, 1 subscription

About: svmpath

Changes:

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


Logo Aleph 0.6

by jiria - January 12, 2009, 20:52:12 CET [ Project Homepage BibTeX Download ] 7190 views, 2093 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 kernlab 0.9-9

by alexis - November 2, 2009, 16:03:50 CET [ Project Homepage BibTeX Download ] 10151 views, 2088 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 OpenCog pre-1.0

by ferrouswheel - January 11, 2009, 22:51:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4606 views, 2081 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 JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5844 views, 2068 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.


Showing Items 101-110 of 552 on page 11 of 56: First Previous 6 7 8 9 10 11 12 13 14 15 16 Next Last