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Showing Items 1-10 of 536 on page 1 of 54: 1 2 3 4 5 6 Next Last

Logo A Parallel LDA Learning Toolbox 1.0

by yanjianfeng - January 24, 2014, 11:48:07 CET [ BibTeX Download ] 639 views, 213 downloads, 1 subscription

About: We introduces PLL, a parallel LDA learning toolbox for big topic modeling.

Changes:

Initial Announcement on mloss.org.


Logo BayesOpt, a Bayesian Optimization toolbox 0.7.1

by rmcantin - July 3, 2014, 00:30:50 CET [ Project Homepage BibTeX Download ] 6494 views, 1363 downloads, 3 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:

-Added MI criterion

-Simplified Python install

-Fixed bugs in annealed criteria


Logo 1SpectralClustering 1.1

by tbuehler - June 27, 2011, 10:45:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6461 views, 1217 downloads, 1 subscription

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

Changes:
  • fixed bug occuring when input graph is disconnected
  • reduced memory usage when input graph has large number of disconnected components
  • more user-friendly usage of main method OneSpectralClustering
  • faster computation of eigenvector initialization + now thresholded according to multicut-criterion
  • several internal optimizations

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 A Pattern Recognizer In Lua with ANNs v0.3.1

by pakozm - May 30, 2014, 10:49:10 CET [ Project Homepage BibTeX Download ] 1988 views, 456 downloads, 2 subscriptions

About: APRIL-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.

Changes:
  • Removed bugs.
  • Added Travis CI support.
  • KNN and clustering algorithms.
  • ZCA and PCA whitening.
  • Quickprop and ASGD optimization algorithms.
  • QLearning trainer.
  • Sparse float matrices are available in CSC an CSR formats.
  • Compilation with Homebrew and MacPorts available.
  • Compilation issues in Ubuntu 12.04 solved.

About: This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classicalmean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.

Changes:

Initial Announcement on mloss.org.


Logo ABACOC Adaptive Ball Cover for Classification 1.0

by kikot - July 14, 2014, 16:27:03 CET [ BibTeX BibTeX for corresponding Paper Download ] 312 views, 61 downloads, 3 subscriptions

About: Online Action Recognition via Nonparametric Incremental Learning. Java and Matlab code already available. A Python version and the Java source code will be released soon.

Changes:

Initial release of the library, future changes will be advertised shortly.


Logo Accord.NET Framework 2.10.0

by cesarsouza - September 9, 2013, 16:12:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14308 views, 2920 downloads, 2 subscriptions

About: Accord.NET provides statistical analysis, machine learning, image processing and computer vision methods for .NET applications. The Accord.NET Framework extends the popular AForge.NET with new features, adding to a more complete environment for scientific computing in .NET.

Changes:

This release aimed to provide improvements to the documentation. Most of the Univariate Distributions now include proper examples for all main functions and measures in their summary page. Also, a wide set of imaging methods, such as Haralick's set of textural features, the Local Binary Pattern, Gabor, Kirsch, and Variance filters have been added. Also includes the Denavit-Hartenberg model for kinematic chains and many updates, optimizations, corrections and bug-fixes in all major namespaces.

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 Aciqra 1.2.1

by Caglow - June 25, 2009, 23:30:22 CET [ BibTeX Download ] 2532 views, 1238 downloads, 1 subscription

About: A desktop planetarium and sky map program which shows the sky from anywhere on Earth at any time.

Changes:

Removed erroneous topocentric code. Increased maximum zoom for detail on planets.


Logo Action Recognition by Dense Trajectories 1.0

by openpr_nlpr - June 6, 2012, 11:38:07 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2914 views, 539 downloads, 1 subscription

About: The code is for computing state-of-the-art video descriptors for action recognition. The most up-to-date information can be found at: http://lear.inrialpes.fr/people/wang/dense_trajectories

Changes:

Initial Announcement on mloss.org.


Showing Items 1-10 of 536 on page 1 of 54: 1 2 3 4 5 6 Next Last