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Showing Items 41-50 of 561 on page 5 of 57: Previous 1 2 3 4 5 6 7 8 9 10 Next Last

Logo JMLR Sally 0.9.2

by konrad - November 19, 2014, 20:28:35 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23047 views, 4676 downloads, 3 subscriptions

About: A Tool for Embedding Strings in Vector Spaces

Changes:

Fixed severe bug in concurrent computation of blended n-grams.


Logo Harry 0.3.2

by konrad - November 19, 2014, 20:24:21 CET [ Project Homepage BibTeX Download ] 3021 views, 654 downloads, 2 subscriptions

About: A Tool for Measuring String Similarity

Changes:

Several minor bugfixes.


Logo WolfeSVM 0.0

by utmath - November 19, 2014, 10:46:11 CET [ Project Homepage BibTeX Download ] 439 views, 100 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 ] 479 views, 93 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 ] 2315 views, 496 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.

Logo semi supervised learning for rgb d object recognition 1.0

by openpr_nlpr - November 4, 2014, 03:24:56 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 691 views, 122 downloads, 1 subscription

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 ] 632 views, 126 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 ] 499 views, 126 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 ] 2535 views, 534 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 41-50 of 561 on page 5 of 57: Previous 1 2 3 4 5 6 7 8 9 10 Next Last