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Showing Items 101-120 of 676 on page 6 of 34: Previous 1 2 3 4 5 6 7 8 9 10 11 Next Last

Logo OpenViBE 0.8.0

by k3rl0u4rn - October 1, 2010, 16:15:08 CET [ Project Homepage BibTeX Download ] 21925 views, 5603 downloads, 0 subscriptions

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About: OpenViBE is an opensource platform that enables to design, test and use Brain-Computer Interfaces (BCI). Broadly speaking, OpenViBE can be used in many real-time Neuroscience applications [...]

Changes:

New release 0.8.0.


Logo UniverSVM 1.22

by fabee - October 16, 2012, 11:24:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 35922 views, 5573 downloads, 0 subscriptions

About: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and data-dependent [...]

Changes:

Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser - ferdinand.kaiser@tut.fi)


Logo libcmaes 0.9.5

by beniz - March 9, 2015, 09:05:22 CET [ Project Homepage BibTeX Download ] 27840 views, 5557 downloads, 0 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.

Changes:

This is a major release, with several novelties, improvements and fixes, among which:

  • step-size two-point adaptaion scheme for improved performances in some settings, ref #88

  • important bug fixes to the ACM surrogate scheme, ref #57, #106

  • simple high-level workflow under Python, ref #116

  • improved performances in high dimensions, ref #97

  • improved profile likelihood and contour computations, including under geno/pheno transforms, ref #30, #31, #48

  • elitist mechanism for forcing best solutions during evolution, ref 103

  • new legacy plotting function, ref #110

  • optional initial function value, ref #100

  • improved C++ API, ref #89

  • Python bindings support with Anaconda, ref #111

  • configure script now tries to detect numpy when building Python bindings, ref #113

  • Python bindings now have embedded documentation, ref #114

  • support for Travis continuous integration, ref #122

  • lower resolution random seed initialization


Logo libnabo 1.0.6

by smagnenat - August 5, 2015, 12:16:40 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 28997 views, 5533 downloads, 0 subscriptions

About: libnabo is a fast K Nearset Neighbor library for low-dimensional spaces.

Changes:
  • Reset point indices of results with distances exceeding threshold (#23, #24)
  • Fine tune the find_package() capability and add uninstall target (#22)
  • Fixed compiler warning (#18)
  • Added OpenMP support (#20, #21)
  • Build type tuning (#19)
  • Fix: terminal comma in enum requires C++11
  • Fix UBSAN error calculating maxNodeCount (#16, #17)
  • Fixed tiny (yet significant) error in the Python doc strings (#15)
  • Compile static lib with PIC (#14)
  • Added configure scripts for full catkinization
  • Catkinization of libnabo (following REP136)
  • Update README.md Added Simon as the maintainer.
  • [test] use CLOCK_PROF for NetBSD build
  • Fixed CppCheck warning. Fix broken install when doxygen is not found
  • Fix cmake stylistic issue
  • Make python install respect custom CMAKE_INSTALL_PREFIX
  • Fix broken install when doxygen is not found

Logo Orange 2.6

by janez - February 14, 2013, 18:15:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 28740 views, 5478 downloads, 0 subscriptions

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About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]

Changes:

The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL.

Changed the BibTeX reference to the paper recently published in JMLR MLOSS.


Logo FABIA 2.8.0

by hochreit - October 18, 2013, 10:14:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27422 views, 5463 downloads, 0 subscriptions

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About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.

Changes:

CHANGES IN VERSION 2.8.0

NEW FEATURES

o rescaling of lapla
o extractPlot does not plot sorted matrices

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

  • spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
  • fabia for non-negative factorization (parameter: non_negative);
  • changed to C and removed dependencies to Rcpp;
  • improved update for lambda (alpha should be smaller, e.g. 0.03);
  • introduced maximal number of row elements (lL);
  • introduced cycle bL when upper bounds nL or lL are effective;
  • reduced computational complexity;
  • bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

  • New option nL: maximal number of biclusters per row element;
  • Sort biclusters according to information content;
  • Improved and extended preprocessing;
  • Update to R2.13

Logo PREA Personalized Recommendation Algorithms Toolkit 1.1

by srcw - September 1, 2012, 22:53:37 CET [ Project Homepage BibTeX Download ] 21464 views, 5411 downloads, 0 subscriptions

About: An open source Java software providing collaborative filtering algorithms.

Changes:

Initial Announcement on mloss.org.


Logo figtree 0.9.2

by vmorariu - January 17, 2009, 00:13:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9124 views, 5410 downloads, 0 subscriptions

About: A library for fast computation of Gauss transforms in multiple dimensions, using the Improved Fast Gauss Transform and a tree data structure. This library is useful for efficient Kernel Density [...]

Changes:

Initial Announcement on mloss.org.


Logo A Local and Parallel Computation Toolbox for Gaussian Process Regression 1.0

by cwpark - March 19, 2012, 17:21:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17482 views, 5406 downloads, 0 subscriptions

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Changes:

Initial Announcement on mloss.org.


Logo KeplerWeka 20101008

by fracpete - October 9, 2010, 05:27:13 CET [ Project Homepage BibTeX Download ] 19132 views, 5368 downloads, 0 subscriptions

About: KeplerWeka represents the integration of all the functionality of the WEKA Machine Learning Workbench into the open-source scientific workflow Kepler. Among them are classification, [...]

Changes:
  • Now compatible with Kepler 2.0
  • New version of WEKA included (patched 3.7.2 release), WEKA's new package manager works in conjunction with Kepler
  • Renamed actor Count to ConditionalTee, introduced new Count actor
  • Removed actors OutputLogger, MultiSync, TwinSync

Logo r-cran-GAMBoost 1.2-2

by r-cran-robot - April 1, 2013, 00:00:04 CET [ Project Homepage BibTeX Download ] 26645 views, 5359 downloads, 0 subscriptions

About: Generalized linear and additive models by likelihood based boosting

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:04.893311


Logo JMLR FastInf 1.0

by arielj - June 4, 2010, 14:04:37 CET [ Project Homepage BibTeX Download ] 15340 views, 5341 downloads, 0 subscriptions

About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm.

Changes:

Initial Announcement on mloss.org.


Logo DeeBNet, a new object oriented MATLAB toolbox for Deep Belief Networks 3.2

by keyvanrad - June 26, 2016, 16:19:55 CET [ Project Homepage BibTeX Download ] 25778 views, 5320 downloads, 0 subscriptions

About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU based, etc. The toolbox is a user-friendly open source software in MATLAB and Octave and is freely available on the website.

Changes:

New in toolbox

  • Using GPU in Backpropagation
  • Revision of some demo scripts
  • Function approximation with multiple outputs
  • Feature extraction with GRBM in first layer

cardinal


Logo r-cran-rattle 2.6.26

by r-cran-robot - March 16, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 23032 views, 5198 downloads, 0 subscriptions

About: Graphical user interface for data mining in R

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:07.700426


Logo The Infinite Hidden Markov Model 0.5

by jvangael - July 21, 2010, 23:41:24 CET [ BibTeX BibTeX for corresponding Paper Download ] 30370 views, 5175 downloads, 0 subscriptions

About: An implementation of the infinite hidden Markov model.

Changes:

Since 0.4: Removed dependency from lightspeed (now using statistics toolbox). Updated to newer matlab versions.


Logo MATLAB spectral clustering package 1.1

by wenyenc - February 4, 2010, 01:54:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27969 views, 5171 downloads, 0 subscriptions

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About: A MATLAB spectral clustering package to deal with large data sets. Our tool can handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. Spectral clustering algorithm has been [...]

Changes:
  • Add bib
  • Add code of nystrom without orthogonalization
  • Add accuracy quality measure
  • Add more running scripts

Logo r-cran-VR 7.2-49

by r-cran-robot - September 25, 2009, 00:00:00 CET [ Project Homepage BibTeX Download ] 21173 views, 5083 downloads, 0 subscriptions

About: VR

Changes:

Fetched by r-cran-robot on 2009-10-03 07:16:05.643423


Logo JProGraM 13.2

by ninofreno - February 13, 2013, 20:29:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 26543 views, 5070 downloads, 0 subscriptions

About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.

Changes:

JProGraM 13.2 -- CHANGE LOG

Release date: February 13, 2012

New features: -- Support for Fiedler random graphs/random field models for large-scale networks (ninofreno.graph.fiedler package); -- Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).


Logo DAL 1.1

by ryota - February 18, 2014, 19:07:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29872 views, 5045 downloads, 0 subscriptions

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/low-rank learning/reconstruction based on the dual augmented Lagrangian method.

Changes:
  • Supports weighted lasso (dalsqal1.m, dallral1.m)
  • Supports weighted squared loss (dalwl1.m)
  • Bug fixes (group lasso and elastic-net-regularized logistic regression)

Logo 1SpectralClustering 1.2

by tbuehler - May 1, 2018, 19:26:07 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 24773 views, 4935 downloads, 0 subscriptions

About: A fast and scalable graph-based clustering algorithm based on the eigenvectors of the nonlinear 1-Laplacian.

Changes:
  • improved optimization of ncut and rcut criterion
  • optimized eigenvector initialization
  • changed default values for number of runs
  • several internal optimizations
  • made console output more informative

Showing Items 101-120 of 676 on page 6 of 34: Previous 1 2 3 4 5 6 7 8 9 10 11 Next Last