All entries.
Showing Items 11-20 of 519 on page 2 of 52: Previous 1 2 3 4 5 6 7 Next Last

Logo SAMOA 0.0.1

by gdfm - April 2, 2014, 17:09:08 CET [ Project Homepage BibTeX Download ] 217 views, 39 downloads, 1 subscription

About: SAMOA is a platform for mining on big data streams. It is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-e1071 1.6-3

by r-cran-robot - February 13, 2014, 00:00:00 CET [ Project Homepage BibTeX Download ] 11671 views, 2400 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 1 vote)

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

Changes:

Fetched by r-cran-robot on 2014-04-01 00:00:04.937452


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 19512 views, 3416 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


Logo Libra 1.0.1

by lowd - March 30, 2014, 09:42:00 CET [ Project Homepage BibTeX Download ] 8697 views, 1810 downloads, 1 subscription

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 1.0.1 (3/30/2014):

  • Several new algorithms -- acmn, learning ACs using MNs; idspn, SPN structure learning; mtlearn, learning mixtures of trees
  • Several new support programs -- spquery, for exact inference in SPNs; spn2ac, for converting SPNs to ACs
  • Renamed aclearnstruct to acbn
  • Replaced aclearnstruct -noac with separate bnlearn program
  • ...and many more small changes and fixes, throughout!

Logo XGBoost v0.1

by crowwork - March 27, 2014, 07:09:52 CET [ Project Homepage BibTeX Download ] 280 views, 45 downloads, 1 subscription

About: eXtreme gradient boosting (tree) library. Features: - Sparse feature format allows easy handling of missing values, and improve computation efficiency. - Efficient parallel implementation that optimizes memory and computation.

Changes:

Initial Announcement on mloss.org.


Logo BayesOpt, a Bayesian Optimization toolbox 0.6

by rmcantin - March 26, 2014, 17:48:17 CET [ Project Homepage BibTeX Download ] 4399 views, 999 downloads, 2 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:

-Complete refactoring of inner parts of the library. The code is easier to understand/modify and it allow simpler integration with new algorithms.

-Updated to the latest version of NLOPT (2.4.1). Wrapper code symplified.

-Error codes replaced with exceptions in C++ interface. Library is exception safe.

-API modified to support new learning methods for kernel hyperparameters (e.g: MCMC). Warning: config parameters about learning have changed. Code using previous versions might not work. Some of the learning methods (like MCMC) are not yet implemented.

-Added configuration of random numbers (can be fixed for debugging). Fixed issue with random numbers using different sources or random number with potential correlations. Now all the elements are guaranteed to use the same instance of the random engine.

-Improved numerical results (e.g.: hyperparameter optimization is done in log space)

-More examples and tests.

-Fixed bugs.

-The number of inner iterations have been increased by default, so overall optimization time using default configuration might be slower, but with improved results.


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 ] 1924 views, 472 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 Chordalysis 1.0

by fpetitjean - March 24, 2014, 01:22:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 235 views, 35 downloads, 1 subscription

About: Log-linear analysis for high-dimensional data

Changes:

Initial Announcement on mloss.org.


Logo ExtRESCAL 0.6

by nzhiltsov - March 21, 2014, 16:22:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1331 views, 273 downloads, 1 subscription

About: Scalable tensor factorization

Changes:
  • Make the extended algorigthm output fixed (by replacing random initialization)
  • Add handling of float values in the extended task
  • Add the util for matrix pseudo inversion
  • Switch to Apache License 2.0

Logo Caffe 0.99

by sergeyk - March 19, 2014, 08:56:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1243 views, 183 downloads, 1 subscription

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

Changes:

Initial Announcement on mloss.org.


Showing Items 11-20 of 519 on page 2 of 52: Previous 1 2 3 4 5 6 7 Next Last