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Showing Items 381-390 of 519 on page 39 of 52: First Previous 34 35 36 37 38 39 40 41 42 43 44 Next Last

Logo Graphical Models and Conditional Random Fields Toolbox 2

by jdomke - January 5, 2012, 15:38:20 CET [ Project Homepage BibTeX Download ] 1947 views, 449 downloads, 1 subscription

About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the high-treewidth setting.

Changes:

Initial Announcement on mloss.org.


Logo GritBot 2.01

by zenog - September 2, 2011, 14:56:26 CET [ Project Homepage BibTeX Download ] 1944 views, 484 downloads, 1 subscription

About: GritBot is an data cleaning and outlier/anomaly detection program.

Changes:

Initial Announcement on mloss.org.


Logo Social Impact theory based Optimizer library 1.0.2

by rishem - March 24, 2014, 08:29:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1943 views, 475 downloads, 1 subscription

About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA.

Changes:

A new variant 'Continuous Opinion Dynamics Optimizer (CODO)' has been implemented in this version. Minor changes in implementation of objective function.


Logo r-cran-bigrf 0.1-6

by r-cran-robot - June 28, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 1906 views, 497 downloads, 0 subscriptions

About: Big Random Forests

Changes:

Fetched by r-cran-robot on 2014-04-01 00:00:04.196492


Logo Uncorrelated Multilinear Principal Component Analysis 1.0

by hplu - June 18, 2012, 17:23:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1896 views, 364 downloads, 1 subscription

About: A Matlab implementation of Uncorrelated Multilinear PCA (UMPCA) for dimensionality reduction of tensor data via tensor-to-vector projection

Changes:

Initial Announcement on mloss.org.


Logo MOSIS 0.55

by claasahl - March 9, 2014, 17:35:40 CET [ BibTeX Download ] 1841 views, 585 downloads, 2 subscriptions

About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications.

Changes:
  • Move "flow"-related classes into package "de.claas.mosis.flow" (e.g. Node and Link).
  • Refined and improved "flow"-related tests (e.g. Iterator and Node tests).
  • Refactored tests for data formats (e.g. PlainText and JSON tests).
  • Added visitor design pattern for graph-based functions (e.g. initialization and processing).
  • Documented parameters of Processor implementations.

Logo QuickDT 0.1

by sanity - September 21, 2011, 13:43:37 CET [ Project Homepage BibTeX Download ] 1841 views, 553 downloads, 1 subscription

About: A decision tree learner that is designed to be reasonably fast, but the primary goal is ease of use

Changes:

Initial Announcement on mloss.org.


Logo KNIME 2.7.4

by toldo - April 29, 2013, 09:14:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1828 views, 351 downloads, 1 subscription

About: A comprehensive data mining environment, with a variety of machine learning components.

Changes:

Modifications following feedback from Knime main Author.


Logo OpenANN 1.1.0

by afabisch - September 26, 2013, 23:52:03 CET [ Project Homepage BibTeX Download ] 1794 views, 378 downloads, 2 subscriptions

About: A library for artificial neural networks.

Changes:

Added algorithms:

  • L-BFGS optimizer
  • k-means
  • sparse auto-encoder
  • preprocessing: normalization, PCA, ZCA whitening

Logo Naive Bayes EM Algorithm 1.0.0

by openpr_nlpr - December 2, 2011, 05:35:09 CET [ Project Homepage BibTeX Download ] 1786 views, 384 downloads, 1 subscription

About: OpenPR-NBEM is an C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. OpenPR-NBEM uses the multinomial event model for representation. The maximum likelihood estimate is used for supervised learning, and the expectation-maximization estimate is used for semi-supervised and un-supervised learning.

Changes:

Initial Announcement on mloss.org.


Showing Items 381-390 of 519 on page 39 of 52: First Previous 34 35 36 37 38 39 40 41 42 43 44 Next Last