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Logo Blocks 0.1

by bartvm - March 30, 2015, 22:25:02 CET [ Project Homepage BibTeX Download ] 110 views, 16 downloads, 1 subscription

About: A Theano framework for building and training neural networks

Changes:

Initial Announcement on mloss.org.


Logo apsis 0.1.1

by fdiehl - March 17, 2015, 08:27:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 388 views, 69 downloads, 2 subscriptions

About: A toolkit for hyperparameter optimization for machine learning algorithms.

Changes:

Initial Announcement on mloss.org.


Logo BayesPy 0.3.2

by jluttine - March 16, 2015, 11:58:37 CET [ Project Homepage BibTeX Download ] 4139 views, 1087 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • Concatenate node added
  • Unit tests for plotting fixed

Logo KeBABS 1.0.5

by UBod - March 4, 2015, 22:34:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2594 views, 441 downloads, 3 subscriptions

About: Kernel-Based Analysis of Biological Sequences

Changes:
  • new accessors selGridRow, selGridCol and fullModel for class ModelSelectionResult
  • change of naming of feature weights because of change in LiblineaR 1.94-2
  • GCC warnings in Linux removed

Logo JMLR Mulan 1.5.0

by lefman - February 23, 2015, 21:19:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16309 views, 6492 downloads, 2 subscriptions

About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions.

Changes:

Learners

  • MLCSSP.java: Added the MLCSSP algorithm (from ICML 2013)
  • Enhancements of multi-target regression capabilities
  • Improved CLUS support
  • Added pairwise classifier and pairwise transformation

Measures/Evaluation

  • Providing training data in the Evaluator is unnecessary in the case of specific measures.
  • Examples with missing ground truth are not skipped for measures that handle missing values.
  • Added logistics and squared error losses and measures

Bug fixes

  • IndexOutOfBounds in calculation of MiAP and GMiAP
  • Bug fix in Rcut.java
  • When in rank/score mode the meta-data contained additional unecessary attributes. (Newton Spolaor)

API changes

  • Upgrade to Java 7
  • Upgrade to Weka 3.7.10

Miscalleneous

  • Small changes and improvements in the wrapper classes for the CLUS library
  • ENTCS13FeatureSelection.java (new experiment)
  • Enumeration is now used for specifying the type of meta-data. (Newton Spolaor)

Logo Machine Learning Support System MALSS 0.5.0

by canard0328 - February 20, 2015, 15:56:02 CET [ Project Homepage BibTeX Download ] 420 views, 92 downloads, 1 subscription

About: MALSS is a python module to facilitate machine learning tasks.

Changes:

Initial Announcement on mloss.org.


About: Learns dynamic network changes across conditions and visualize the results in Cytoscape.

Changes:

Initial Announcement on mloss.org.


Logo ExtRESCAL 0.7.2

by nzhiltsov - January 20, 2015, 00:35:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4197 views, 771 downloads, 2 subscriptions

About: Scalable tensor factorization

Changes:
  • Improve (speed up) initialization of A by summation

Logo pyGPs 1.3.2

by mn - January 17, 2015, 13:08:43 CET [ Project Homepage BibTeX Download ] 3975 views, 938 downloads, 4 subscriptions

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

Changes:

Changelog pyGPs v1.3.2

December 15th 2014

  • pyGPs added to pip
  • mathematical definitions of kernel functions available in documentation
  • more error message added

Logo LRSLibrary 1.0.2

by andrewssobral - January 16, 2015, 18:29:54 CET [ Project Homepage BibTeX Download ] 435 views, 100 downloads, 2 subscriptions

About: Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos.

Changes:

Initial Announcement on mloss.org.


Logo bayes scala 0.5-SNAPSHOT

by danielkorzekwa - January 9, 2015, 19:23:48 CET [ Project Homepage BibTeX Download ] 573 views, 132 downloads, 2 subscriptions

About: It is a Scala library for building Bayesian Networks with discrete/continuous variables and running deterministic Bayesian inference

Changes:

Initial Announcement on mloss.org.


Logo JMLR MLPACK 1.0.12

by rcurtin - January 7, 2015, 19:23:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 39574 views, 7711 downloads, 6 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • Switch to 3-clause BSD license.

Logo JEMLA 1.0

by bathaeian - January 4, 2015, 08:34:49 CET [ Project Homepage BibTeX Download ] 465 views, 120 downloads, 2 subscriptions

About: Java package for calculating Entropy for Machine Learning Applications

Changes:

An initial user guide is ready now. Please download that at the homepage of project: http://profs.basu.ac.ir/bathaeian/free_space JEMLA is now on the GitHub too: https://github.com/bathaeian/JEMLA


Logo APCluster 1.4.1

by UBod - December 10, 2014, 12:58:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20541 views, 3735 downloads, 3 subscriptions

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About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

Changes:
  • fixes in C++ code of sparse affinity propagation

Logo Accord.NET Framework 2.14.0

by cesarsouza - December 9, 2014, 23:04:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18745 views, 3901 downloads, 2 subscriptions

About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.

Changes:

Adding a large number of new distributions, such as Anderson-Daring, Shapiro-Wilk, Inverse Chi-Square, Lévy, Folded Normal, Shifted Log-Logistic, Kumaraswamy, Trapezoidal, U-quadratic and BetaPrime distributions, Birnbaum-Saunders, Generalized Normal, Gumbel, Power Lognormal, Power Normal, Triangular, Tukey Lambda, Logistic, Hyperbolic Secant, Degenerate and General Continuous distributions.

Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.

For a complete list of changes, please see the full release notes at the release details page at:

https://github.com/accord-net/framework/releases


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.5

by hn - December 8, 2014, 13:54:38 CET [ Project Homepage BibTeX Download ] 22541 views, 5234 downloads, 3 subscriptions

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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

Changes:
  • mechanism for specifying hyperparameter priors (together with Roman Garnett and José Vallet)
  • new inference method inf/infGrid allowing efficient inference for data defined on a Cartesian grid (together with Andrew Wilson)
  • new mean/cov functions for preference learning: meanPref/covPref
  • new mean/cov functions for non-vectorial data: meanDiscrete/covDiscrete
  • new piecewise constant nearest neighbor mean function: meanNN
  • new mean functions being predictions from GPs: meanGP and meanGPexact
  • new covariance function for standard additive noise: covEye
  • new covariance function for factor analysis: covSEfact
  • new covariance function with varying length scale : covSEvlen
  • make covScale more general to scaling with a function instead of a scalar
  • bugfix in covGabor* and covSM (due to Andrew Gordon Wilson)
  • bugfix in lik/likBeta.m (suggested by Dali Wei)
  • bugfix in solve_chol.c (due to Todd Small)
  • bugfix in FITC inference mode (due to Joris Mooij) where the wrong mode for post.L was chosen when using infFITC and post.L being a diagonal matrix
  • bugfix in infVB marginal likelihood for likLogistic with nonzero mean function (reported by James Lloyd)
  • removed the combination likErf/infVB as it yields a bad posterior approximation and lacks theoretical justification
  • Matlab and Octave compilation for L-BFGS-B v2.4 and the more recent L-BFGS-B v3.0 (contributed by José Vallet)
  • smaller bugfixes in gp.m (due to Joris Mooij and Ernst Kloppenburg)
  • bugfix in lik/likBeta.m (due to Dali Wei)
  • updated use of logphi in lik/likErf
  • bugfix in util/solve_chol.c where a typing issue occured on OS X (due to Todd Small)
  • bugfix due to Bjørn Sand Jensen noticing that cov_deriv_sq_dist.m was missing in the distribution
  • bugfix in infFITC_EP for ttau->inf (suggested by Ryan Turner)

Logo The Statistical ToolKit 0.8.4

by joblion - December 5, 2014, 13:21:47 CET [ Project Homepage BibTeX Download ] 1058 views, 409 downloads, 2 subscriptions

About: STK++: A Statistical Toolkit Framework in C++

Changes:

Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix


Logo RLPy 1.3a

by bobklein2 - August 28, 2014, 14:34:35 CET [ Project Homepage BibTeX Download ] 2550 views, 583 downloads, 1 subscription

About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting).

Changes:
  • Fixed bug where results using same random seed were different with visualization turned on/off
  • Created RLPy package on pypi (Available at https://pypi.python.org/pypi/rlpy)
  • Switched from custom logger class to python default
  • Added unit tests
  • Code readability improvements (formatting, variable names/ordering)
  • Restructured TD Learning heirarchy
  • Updated tutorials

Logo Toeblitz Toolkit for Fast Toeplitz Matrix Operations 1.03

by cunningham - August 13, 2014, 02:21:36 CET [ BibTeX Download ] 3256 views, 832 downloads, 2 subscriptions

About: Toeblitz is a MATLAB/Octave package for operations on positive definite Toeplitz matrices. It can solve Toeplitz systems Tx = b in O(n*log(n)) time and O(n) memory, compute matrix inverses T^(-1) (with free log determinant) in O(n^2) time and memory, compute log determinants (without inverses) in O(n^2) time and O(n) memory, and compute traces of products A*T for any matrix A, in minimal O(n^2) time and memory.

Changes:

Adding a write-up in written/toeblitz.pdf describing the package.


Logo JMLR GPstuff 4.5

by avehtari - July 22, 2014, 14:03:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18340 views, 4467 downloads, 2 subscriptions

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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2014-07-22 Version 4.5

New features

  • Input dependent noise and signal variance.

    • Tolvanen, V., Jylänki, P. and Vehtari, A. (2014). Expectation Propagation for Nonstationary Heteroscedastic Gaussian Process Regression. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, accepted for publication. Preprint http://arxiv.org/abs/1404.5443
  • Sparse stochastic variational inference model.

    • Hensman, J., Fusi, N. and Lawrence, N. D. (2013). Gaussian processes for big data. arXiv preprint http://arxiv.org/abs/1309.6835.
  • Option 'autoscale' in the gp_rnd.m to get split normal approximated samples from the posterior predictive distribution of the latent variable.

    • Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica, 57(6):1317-1339.

    • Villani, M. and Larsson, R. (2006). The Multivariate Split Normal Distribution and Asymmetric Principal Components Analysis. Communications in Statistics - Theory and Methods, 35(6):1123-1140.

Improvements

  • New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported).
  • Precomputed demo results (including the figures) are now available in the folder tests/realValues.
  • New demos demonstrating new features etc.
    • demo_epinf, demonstrating the input dependent noise and signal variance model
    • demo_svi_regression, demo_svi_classification
    • demo_modelcomparison2, demo_survival_comparison

Several minor bugfixes


Showing Items 1-20 of 85 on page 1 of 5: 1 2 3 4 5 Next