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Showing Items 81-90 of 588 on page 9 of 59: First Previous 4 5 6 7 8 9 10 11 12 13 14 Next Last

Logo Optunity 1.0.1

by claesenm - December 2, 2014, 15:11:47 CET [ Project Homepage BibTeX Download ] 1881 views, 513 downloads, 1 subscription

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.

Changes:

Bugfixes related to Python 3. Added smoke tests for all solvers to prevent similar issues in the future.


Logo linearizedGP 1.0

by dsteinberg - November 28, 2014, 07:02:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 902 views, 191 downloads, 1 subscription

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.

Changes:

Initial Announcement on mloss.org.


Logo WolfeSVM 0.0

by utmath - November 19, 2014, 10:46:11 CET [ Project Homepage BibTeX Download ] 765 views, 190 downloads, 2 subscriptions

About: This is a library for solving nu-SVM by using Wolfe's minimum norm point algorithm. You can solve binary classification problem.

Changes:

Initial Announcement on mloss.org.


Logo Lynx MATLAB Toolbox v0.8-beta

by ispamm - November 19, 2014, 00:56:07 CET [ Project Homepage BibTeX Download ] 867 views, 219 downloads, 1 subscription

About: A MATLAB toolbox for defining complex machine learning comparisons

Changes:

Initial Announcement on mloss.org.


Logo Lua MapReduce v0.3.6

by pakozm - November 15, 2014, 13:20:01 CET [ Project Homepage BibTeX Download ] 3174 views, 738 downloads, 3 subscriptions

About: Lua-MapReduce framework implemented in Lua using luamongo driver and MongoDB as storage. It follows Iterative MapReduce for training of Machine Learning statistical models.

Changes:
  • Improved tuple implementation.

About: This provide a semi-supervised learning method based co-training for RGB-D object recognition. Besides, we evaluate four state-of-the-art feature learing method under the semi-supervised learning framework.

Changes:

Initial Announcement on mloss.org.


Logo libcluster 2.1

by dsteinberg - October 31, 2014, 23:27:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1151 views, 273 downloads, 2 subscriptions

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

Changes:

Initial Announcement on mloss.org.


Logo LogRegCrowds, Logistic Regression from Crowds 1.0

by fmpr - October 30, 2014, 19:10:23 CET [ Project Homepage BibTeX Download ] 843 views, 233 downloads, 2 subscriptions

About: LogReg-Crowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979).

Changes:

Initial Announcement on mloss.org.


About: This library implements the Optimum-Path Forest classifier for unsupervised and supervised learning.

Changes:

Initial Announcement on mloss.org.


Logo pySPACE 1.2

by krell84 - October 29, 2014, 15:36:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3293 views, 698 downloads, 1 subscription

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.

Changes:

improved testing, improved documentation, windows compatibility, more algorithms


Showing Items 81-90 of 588 on page 9 of 59: First Previous 4 5 6 7 8 9 10 11 12 13 14 Next Last