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Logo r-cran-GMMBoost 1.0.3

by r-cran-robot - September 27, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 2936 views, 551 downloads, 0 subscriptions

About: Likelihood-based Boosting for Generalized mixed models

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.366545


Logo OpenGM 2 2.0.2 beta

by opengm - June 1, 2012, 14:33:53 CET [ Project Homepage BibTeX Download ] 2426 views, 551 downloads, 1 subscription

About: A C++ Library for Discrete Graphical Models

Changes:

Initial Announcement on mloss.org.


Logo Chalearn gesture challenge code by jun wan 1.0

by joewan - September 11, 2013, 07:32:51 CET [ BibTeX BibTeX for corresponding Paper Download ] 2274 views, 550 downloads, 0 subscriptions

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT, EMoSIFT and SMoSIFT features.

Changes:

Initial Announcement on mloss.org.


Logo Hivemall 0.1

by myui - October 25, 2013, 08:43:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3556 views, 549 downloads, 1 subscription

About: Hivemall is a scalable machine learning library running on Hive/Hadoop, licensed under the LGPL 2.1.

Changes:
  • Enhancement

    • Added AROW regression
    • Added AROW with a hinge loss (arowh_regress())
  • Bugfix

    • Fixed a bug of null feature handling in classification/regression

Logo OpenANN 1.1.0

by afabisch - September 26, 2013, 23:52:03 CET [ Project Homepage BibTeX Download ] 2590 views, 549 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 BayesPy 0.2.1

by jluttine - September 30, 2014, 16:35:11 CET [ Project Homepage BibTeX Download ] 1877 views, 547 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • Add workaround for matplotlib 1.4.0 bug related to interactive mode which affected monitoring

  • Fix bugs in Hinton diagrams for Gaussian variables


Logo Multilinear Principal Component Analysis 1.2 1.2

by openpr_nlpr - April 16, 2012, 09:04:08 CET [ Project Homepage BibTeX Download ] 1810 views, 545 downloads, 1 subscription

About: This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-penalizedSVM 1.1

by r-cran-robot - August 2, 2010, 00:00:00 CET [ Project Homepage BibTeX Download ] 2618 views, 545 downloads, 0 subscriptions

About: Feature Selection SVM using penalty functions

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:07.509844


Logo pyGPs 1.3

by mn - October 20, 2014, 16:03:28 CET [ Project Homepage BibTeX Download ] 2390 views, 544 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 Graphical Models and Conditional Random Fields Toolbox 2

by jdomke - January 5, 2012, 15:38:20 CET [ Project Homepage BibTeX Download ] 2281 views, 542 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.


Showing Items 391-400 of 542 on page 40 of 55: First Previous 35 36 37 38 39 40 41 42 43 44 45 Next Last