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Logo pyGPs 1.3

by mn - October 20, 2014, 16:03:28 CET [ Project Homepage BibTeX Download ] 2658 views, 604 downloads, 3 subscriptions

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning.

Changes:

Changelog pyGPs v1.3

October 19th 2014

documentation updates:

  • DOC: model.fit() is now named model.getPosterior
  • DOC: typo fixed: cov.LIN changed to cov.Linear
  • DOC: removed cov.Periodic() in demos because its limited in 1-d data.
  • API file updated accordingly

structural updates:

  • removed unused ScalePrior attribute in most inference method
  • added function jitchol, which added a small jitter(instead of doing Cholesky decomposition directly) to the diagonal when the kernel matrix is ill conditioned.
  • thrown error when using periodic covariance functions for non-1d data. We also removed cov.Periodic() in demos because its limited usage.
  • combined equally spaced positions of inputs as test positions as well in plot methods to get a more accurate plotting.
  • rename model.fit() to model.getPosterior(), while model.optimize() stays the same. (since it is confusing for some users that the name fit() is not doing optimizing.)

Logo CFSPCommunityDetection 1.0

by tbuehler - October 13, 2014, 05:36:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 529 views, 121 downloads, 1 subscription

About: A community detection method based on constrained fractional set programming (CFSP).

Changes:

Initial Announcement on mloss.org.


Logo ExtRESCAL 0.7.1

by nzhiltsov - October 11, 2014, 17:08:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3004 views, 578 downloads, 1 subscription

About: Scalable tensor factorization

Changes:
  • Grealy improve the memory consumption for all scripts after refactoring to using csr_matrix
  • Fix the eigenvalue initialization

Logo BayesOpt, a Bayesian Optimization toolbox 0.7.2

by rmcantin - October 10, 2014, 19:12:59 CET [ Project Homepage BibTeX Download ] 9461 views, 1942 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


Logo AugmentedSVM 1.0.0

by ashukla - October 2, 2014, 11:24:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 736 views, 144 downloads, 2 subscriptions

About: A MATLAB toolkit for performing generalized regression with equality/inequality constraints on the function value/gradient.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-e1071 1.6-4

by r-cran-robot - September 1, 2014, 00:00:00 CET [ Project Homepage BibTeX Download ] 14401 views, 3030 downloads, 1 subscription

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About: Misc Functions of the Department of Statistics (e1071), TU Wien

Changes:

Fetched by r-cran-robot on 2014-11-01 00:00:04.932716


Logo hca 0.61

by wbuntine - September 10, 2014, 03:33:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5455 views, 1059 downloads, 4 subscriptions

About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls.

Changes:

Corrections to diagnostics and topic report. Correction to estimating alpha. Now estimating beta sometimes (when estimating phi).


Logo Somoclu 1.4

by peterwittek - September 5, 2014, 13:01:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4983 views, 937 downloads, 2 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes.

Changes:
  • Better Windows support.
  • Completed CUDA support for Python and R interfaces.
  • Faster compilation by removing unnecessary flags for nvcc
  • Support for CUDA 6.5.
  • Bug fixes: R version no longer needs separate code.

Logo JMLR Darwin 1.8

by sgould - September 3, 2014, 08:42:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 30219 views, 6323 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.

Changes:

Version 1.8:

  • Added Superpixel Graph Label Transfer (nnGraph) Project project
  • Added Python scripts for automating some projects
  • Added ability to pre-process features on-the-fly with one drwnFeatureTransform when applying or learning another drwnFeatureTransform
  • Fixed race condition in Windows threading (thanks to Edison Guo)
  • Switched Windows and Linux to build against OpenCV 2.4.9
  • Changed drwnMAPInference::inference to return upper and lower energy bounds
  • Added pruneRounds function to drwnBoostedClassifier
  • Added drwnSLICSuperpixels function
  • Added drwnIndexQueue class
  • mexLearnClassifier and mexAnalyseClassifier now support integer label types
  • Bug fix in mexSaveSuperpixels to support single channel

Logo XGBoost v0.3.0

by crowwork - September 2, 2014, 02:43:31 CET [ Project Homepage BibTeX Download ] 3087 views, 582 downloads, 2 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily.

Changes:

New features: - R support that is now on CRAN

  • Faster tree construction module

  • Support for boosting from initial predictions

  • Linear booster is now parallelized, using parallel coordinated descent.


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