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Logo JMLR RL library 3.00.00

by frezza - January 13, 2015, 04:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5672 views, 1667 downloads, 2 subscriptions

About: A template based C++ reinforcement learning library

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Initial Announcement on mloss.org.


Logo Marray 2.2

by andres - July 6, 2011, 01:27:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5585 views, 1296 downloads, 1 subscription

About: Fast Runtime-Flexible Multi-dimensional Arrays and Views for C++

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Initial Announcement on mloss.org.


Logo r-cran-caretNWS 0.25

by r-cran-robot - December 3, 2008, 00:00:00 CET [ Project Homepage BibTeX Download ] 5570 views, 1368 downloads, 1 subscription

About: Classification and Regression Training in Parallel Using NetworkSpaces: Augment some caret functions using parallel processing

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Initial Announcement on mloss.org.


Logo JMLR BudgetedSVM v1.1

by nemanja - February 12, 2014, 20:53:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5569 views, 983 downloads, 1 subscription

About: BudgetedSVM is an open-source C++ toolbox for scalable non-linear classification. The toolbox can be seen as a missing link between LibLinear and LibSVM, combining the efficiency of linear with the accuracy of kernel SVM. We provide an Application Programming Interface for efficient training and testing of non-linear classifiers, supported by data structures designed for handling data which cannot fit in memory. We also provide command-line and Matlab interfaces, providing users with an efficient, easy-to-use tool for large-scale non-linear classification.

Changes:

Changed license from LGPL v3 to Modified BSD.


Logo Ohmm 0.02

by hillbig - May 21, 2009, 10:07:53 CET [ Project Homepage BibTeX Download ] 5561 views, 1556 downloads, 1 subscription

About: Ohmm is a library for learning hidden Markov models by using Online EM algorithm. This library is specialized for large scale data; e.g. 1 million words. The output includes parameters, and estimation results.

Changes:

Initial Announcement on mloss.org.


Logo Market Basket Synthetic Data Generator v1.0.0.0

by apitman - February 9, 2011, 11:26:55 CET [ Project Homepage BibTeX Download ] 5558 views, 1378 downloads, 1 subscription

About: An open-source C# market-basket synthetic data generator, capable of creating transactions, sequences and taxonomies, based on the IBM Quest version. Written to address the maintainability and portability problems of the original, feedback, fixes and extensions are encouraged!

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Initial Announcement on mloss.org.


Logo Variational Bayesian Linear Gaussian State-Space Models 1

by silviac - November 10, 2007, 22:06:26 CET [ Project Homepage BibTeX Download ] 5558 views, 1554 downloads, 0 subscriptions

About:

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Logo Piqle 2.0

by fdecomite - June 19, 2009, 10:16:53 CET [ Project Homepage BibTeX Download ] 5542 views, 2340 downloads, 1 subscription

About: Piqle (Platform for Implementing Q-Learning Experiments) is a Java framework for fast design, prototyping and test of reinforcement learning experiments (RL). By clearly separating algorithms and problems, it allows users to focus on either part of the RL paradigm:designing new algorithms or implementing new problems. Piqle implements many classical RL algorithms, making their parameters easily tunable. At this time, 13 problems are implemented, several with one or more variants. The user's manual explains in detail how to code a new problem. Written in Java, Piqle is as platform-independent as Java itself. Its components can easily be embedded as part of complex implementations, like robotics or decision making.

Changes:

Initial Announcement on mloss.org.


Logo Ankus 0.0.1

by suhyunjeon - September 13, 2013, 06:47:46 CET [ Project Homepage BibTeX Download ] 5540 views, 1001 downloads, 1 subscription

About: Ankus is an open source data mining / machine learning based MapReduce that supports a variety of advanced algorithms.

Changes:

Initial Announcement on mloss.org.


About: We study the problem of robust feature extraction based on L21 regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an L21-norm minimization can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify L1-norm and L21-norm minimization within a common framework. In algorithmic part, we propose an L21 regularized correntropy algorithm to extract informative features meanwhile to remove outliers from training data. A new alternate minimization algorithm is also developed to optimize the non-convex correntropy objective. In terms of face recognition, we apply the proposed method to obtain an appearance-based model, called Sparse-Fisherfaces. Extensive experiments show that our method can select robust and sparse features, and outperforms several state-of-the-art subspace methods on largescale and open face recognition datasets.

Changes:

Initial Announcement on mloss.org.


Showing Items 401-410 of 658 on page 41 of 66: First Previous 36 37 38 39 40 41 42 43 44 45 46 Next Last