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Showing Items 71-80 of 595 on page 8 of 60: First Previous 3 4 5 6 7 8 9 10 11 12 13 Next Last

Logo JMLR SSA Toolbox 1.3

by paulbuenau - January 24, 2012, 15:51:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15813 views, 4594 downloads, 1 subscription

About: The SSA Toolbox is an efficient, platform-independent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab. Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of non-stationary data, i.e. data whose statistical properties change over time. SSA helps to detect, investigate and visualize temporal changes in complex high-dimensional data sets.

Changes:
  • Various bugfixes.

Logo r-cran-glmnet 1.9-3

by r-cran-robot - March 1, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 15754 views, 3460 downloads, 1 subscription

About: Lasso and elastic-net regularized generalized linear models

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.081872


Logo python weka wrapper 0.3.2

by fracpete - June 28, 2015, 23:09:13 CET [ Project Homepage BibTeX Download ] 15701 views, 3376 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • The "packages" parameter of the "weka.core.jvm.start()" function can be used for specifying an alternative Weka home directory now as well
  • added "train_test_split" method to "weka.core.Instances" class to easily create train/test splits
  • "evaluate_train_test_split" method of "weka.classifiers.Evaluation" class now uses the "train_test_split" method

Logo DAL 1.1

by ryota - February 18, 2014, 19:07:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15666 views, 2641 downloads, 1 subscription

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/low-rank learning/reconstruction based on the dual augmented Lagrangian method.

Changes:
  • Supports weighted lasso (dalsqal1.m, dallral1.m)
  • Supports weighted squared loss (dalwl1.m)
  • Bug fixes (group lasso and elastic-net-regularized logistic regression)

Logo Malheur 0.5.4

by konrad - December 25, 2013, 13:20:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15344 views, 2941 downloads, 1 subscription

About: Automatic Analysis of Malware Behavior using Machine Learning

Changes:

Support for new version of libarchive. Minor bug fixes.


Logo Libra 1.1.2c

by lowd - June 25, 2015, 00:10:25 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15032 views, 3278 downloads, 3 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 1.1.2c (6/24/2015):

  • Libra can now be installed via OPAM as well. To install OPAM, see: http://opam.ocaml.org/doc/Install.html . Then run: "opam install libra-tk".
  • Updated documentation to describe OPAM installation.

Logo r-cran-klaR 0.6-8

by r-cran-robot - March 27, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 14815 views, 3134 downloads, 1 subscription

About: Classification and visualization

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.722314


Logo BayesOpt, a Bayesian Optimization toolbox 0.7.2

by rmcantin - October 10, 2014, 19:12:59 CET [ Project Homepage BibTeX Download ] 14798 views, 2947 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


About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others.

Changes:
  • Modified specification of optional parameters (using sfo_opt)
  • Added sfo_ls_lazy for maximizing nonnegative submodular functions
  • Added sfo_fn_infogain, sfo_fn_lincomb, sfo_fn_invert, ...
  • Added additional documentation and more examples
  • Now Octave ready

Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14209 views, 5489 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


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