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Showing Items 81-90 of 582 on page 9 of 59: First Previous 4 5 6 7 8 9 10 11 12 13 14 Next Last

Logo Malheur 0.5.4

by konrad - December 25, 2013, 13:20:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14456 views, 2786 downloads, 1 subscription

About: Automatic Analysis of Malware Behavior using Machine Learning

Changes:

Support for new version of libarchive. Minor bug fixes.


Logo Maja Machine Learning Framework 1.0

by jhm - September 13, 2011, 15:13:56 CET [ Project Homepage BibTeX Download ] 13254 views, 2760 downloads, 1 subscription

About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents.

Changes:
  • Experiments can now be invoked from the command line
  • Experiments can now be "scripted"
  • MMLF Experimenter contains now basic module for statistical hypothesis testing
  • MMLF Explorer can now visualize the model that has been learned by an agent

Logo Libra 1.1.1

by lowd - May 22, 2015, 10:16:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12263 views, 2670 downloads, 2 subscriptions

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees.

Changes:

Version 1.1.1 (5/21/2015):

  • Many minor fixes to documentation, scripts, and code.

Logo JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7468 views, 2667 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 Pyriel 1.5

by tfawcett - October 27, 2010, 09:12:53 CET [ BibTeX BibTeX for corresponding Paper Download ] 12282 views, 2663 downloads, 1 subscription

About: Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but it's robust and fast enough to be used for lightweight industrial data mining.

Changes:

1.5 Changed CF (confidence factor) to do LaPlace smoothing of estimates. New flag "--score-for-class C" causes scores to be computed relative to a given (positive) class. For two-class problems. Fixed bug in example sampling code (--sample n) Fixed bug keeping old-style example formats (terminated by dot) from working. More code restructuring.


Logo r-cran-ipred 0.9-1

by r-cran-robot - November 14, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 11209 views, 2662 downloads, 1 subscription

About: Improved Predictors

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.613011


Logo r-cran-tgp 2.4-3

by r-cran-robot - December 18, 2011, 00:00:00 CET [ Project Homepage BibTeX Download ] 12560 views, 2648 downloads, 1 subscription

About: Bayesian treed Gaussian process models

Changes:

Fetched by r-cran-robot on 2012-02-01 00:00:11.834310


Logo GibbsLDA 0.2

by pxhieu - May 9, 2008, 22:18:52 CET [ Project Homepage BibTeX Download ] 6004 views, 2641 downloads, 1 subscription

About: GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for parameter estimation and inference. GibbsLDA++ is fast and is designed to analyze hidden/latent topic [...]

Changes:

Initial Announcement on mloss.org.


Logo SimpleMKL 0.5

by arakotom - June 11, 2008, 00:56:47 CET [ Project Homepage BibTeX Download ] 10031 views, 2625 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 2 votes)

About: Matlab Multiple Kernel Learning toolbox. Features : MKL for SVM Classification, Regression and MultiClass. It needs SVM-KM Toolbox

Changes:

Initial Announcement on mloss.org.


Logo BayesOpt, a Bayesian Optimization toolbox 0.7.2

by rmcantin - October 10, 2014, 19:12:59 CET [ Project Homepage BibTeX Download ] 12720 views, 2596 downloads, 4 subscriptions

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python.

Changes:

-Fixed bugs and doc typos


Showing Items 81-90 of 582 on page 9 of 59: First Previous 4 5 6 7 8 9 10 11 12 13 14 Next Last