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Logo r-cran-caret 6.0-73

by r-cran-robot - November 8, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 116679 views, 22169 downloads, 3 subscriptions

About: Classification and Regression Training

Changes:

Fetched by r-cran-robot on 2017-03-01 00:00:03.046793


Logo Tools for Regression and Classification 1.0.0

by matloff - October 29, 2016, 08:22:40 CET [ Project Homepage BibTeX Download ] 1507 views, 231 downloads, 3 subscriptions

About: Toolkit for parametric and nonparametric regression and classification.

Changes:

Initial Announcement on mloss.org.


Logo rectools a Novel Toolbox for Recommender Systems 1.0.0

by matloff - October 29, 2016, 07:41:58 CET [ Project Homepage BibTeX Download ] 1353 views, 244 downloads, 2 subscriptions

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About: Novel R toolbox for collaborative filtering recommender systems.

Changes:

Initial Announcement on mloss.org.


Logo DIANNE 0.5.0

by sbohez - October 25, 2016, 19:51:07 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1308 views, 217 downloads, 3 subscriptions

About: DIANNE is a modular software framework for designing, training and evaluating artificial neural networks on heterogeneous, distributed infrastructure . It is built on top of OSGi and AIOLOS and can transparently deploy and redeploy (parts of) a neural network on multiple machines, as well as scale up training on a compute cluster.

Changes:

Initial Announcement on mloss.org.


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 4.0

by hn - October 19, 2016, 10:15:05 CET [ Project Homepage BibTeX Download ] 41748 views, 9258 downloads, 5 subscriptions

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About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models.

Changes:

A major code restructuring effort did take place in the current release unifying certain inference functions and allowing more flexibility in covariance function composition. We also redesigned the whole derivative computation pipeline to strongly improve the overall runtime. We finally include grid-based covariance approximations natively.

More generic sparse approximation using Power EP

  • unified treatment of FITC approximation, variational approaches VFE and hybrids

  • inducing input optimisation for all (compositions of) covariance functions dropping the previous limitation to a few standard examples

  • infFITC is now covered by the more generic infGaussLik function

Approximate covariance object unifying sparse approximations, grid-based approximations and exact covariance computations

  • implementation in cov/apx, cov/apxGrid, cov/apxSparse

  • generic infGaussLik unifies infExact, infFITC and infGrid

  • generic infLaplace unifies infLaplace, infFITC_Laplace and infGrid_Laplace

Hiearchical structure of covariance functions

  • clear hierachical compositional implementation

  • no more code duplication as present in covSEiso and covSEard pairs

  • two mother covariance functions

    • covDot for dot-product-based covariances and

    • covMaha for Mahalanobis-distance-based covariances

  • a variety of modifiers: eye, iso, ard, proj, fact, vlen

  • more flexibility as more variants are available and possible

  • all covariance functions offer derivatives w.r.t. inputs

Faster derivative computations for mean and cov functions

  • switched from partial derivatives to directional derivatives

  • simpler and more concise interface of mean and cov functions

  • much faster marginal likelihood derivative computations

  • simpler and more compact code

New mean functions

  • new mean/meanWSPC (Weighted Sum of Projected Cosines or Random Kitchen Sink features) following a suggestion by William Herlands

  • new mean/meanWarp for constructing a new mean from an existing one by means of a warping function adapted from William Herlands

New optimizer

  • added a new minimize_minfunc, contributed by Truong X. Nghiem

New GLM link function

  • added the twice logistic link function util/glm_invlink_logistic2

Smaller fixes

  • two-fold speedup of util/elsympol used by covADD by Truong X. Nghiem

  • bugfix in util/logphi as reported by John Darby


Logo AMIDST Toolbox 0.6.0

by ana - October 14, 2016, 19:35:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6023 views, 1056 downloads, 4 subscriptions

About: A Java Toolbox for Scalable Probabilistic Machine Learning.

Changes:
  • Added sparklink module implementing the integration with Apache Spark. More information here.
  • Fluent pattern in latent-variable-models
  • Predefined model implementing the concept drift detection

Detailed information can be found in the toolbox's web page


Logo r-cran-arules 1.5-0

by r-cran-robot - September 23, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 38105 views, 7971 downloads, 3 subscriptions

About: Mining Association Rules and Frequent Itemsets

Changes:

Fetched by r-cran-robot on 2017-03-01 00:00:02.515745


Logo RLScore 0.7

by aatapa - September 20, 2016, 09:51:25 CET [ Project Homepage BibTeX Download ] 1216 views, 266 downloads, 3 subscriptions

About: RLScore - regularized least-squares machine learning algorithms package

Changes:

Initial Announcement on mloss.org.


Logo r-cran-bst 0.3-14

by r-cran-robot - September 12, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 7300 views, 1680 downloads, 2 subscriptions

About: Gradient Boosting

Changes:

Fetched by r-cran-robot on 2017-03-01 00:00:02.912215


Logo slim for matlab 0.2

by ustunb - August 23, 2016, 20:27:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2146 views, 347 downloads, 3 subscriptions

About: learn optimized scoring systems using MATLAB and the CPLEX Optimization Studio

Changes:

Initial Announcement on mloss.org.


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