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Logo monte python 0.1.0

by roro - May 9, 2008, 21:45:47 CET [ Project Homepage BibTeX Download ] 5491 views, 2150 downloads, 1 subscription

About: Monte (python) is a small machine learning library written in pure Python. The focus is on gradient based learning, in particular on the construction of complex models from many smaller components.

Changes:

Initial Announcement on mloss.org.


Logo Spike train feature extraction by Bayesian binning 0.1

by dendres - September 24, 2008, 16:19:14 CET [ BibTeX BibTeX for corresponding Paper Download ] 5478 views, 1308 downloads, 0 comments, 1 subscription

About: *binsdfc* is a command line implementation of the algorithm described in [Endres,Oram,Schindelin,Foldiak:*Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms*, [...]

Changes:

Changed build system from automake to cmake. Moved download page to www.compsens.uni-tuebingen.de


Logo Spider 1.71

by jaseweston - November 19, 2007, 15:51:59 CET [ Project Homepage BibTeX Download ] 5476 views, 1658 downloads, 0 subscriptions

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About: The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be [...]

Changes:

Initial Announcement on mloss.org.


Logo SeDuMi 1.21

by sonne - July 13, 2009, 10:22:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5469 views, 1119 downloads, 1 subscription

About: SeDuMi is a software package to solve optimization problems over symmetric cones. This includes linear, quadratic, second order conic and semidefinite optimization, and any combination of these.

Changes:

Initial Announcement on mloss.org.


Logo ILNumerics.Net 1.4.01

by haymo - October 14, 2008, 01:24:28 CET [ Project Homepage BibTeX Download ] 5461 views, 1319 downloads, 1 subscription

About: Intended for .NET developers wanting to implement algorithms directly in a common .NET language (recommended: C#). Support for n-dim generic arrays, LAPACK, FFT, cells, logicals, 2D&3D plotting [...]

Changes:

Initial Announcement on mloss.org.


Logo TiMBL 6.1

by antalvdb - January 11, 2008, 09:20:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5437 views, 1233 downloads, 0 comments, 0 subscriptions

About: The TiMBL software package is a fast, decision-tree-based implementation of k-nearest neighbor classification. The package includes the IB1, IB2, TRIBL, TRIBL2, and IGTree algorithms, and offers [...]

Changes:

Initial Announcement on mloss.org.


Logo Local Alignment Kernels 0.3.2

by hiroto - December 1, 2007, 00:10:23 CET [ Project Homepage BibTeX Download ] 5411 views, 1276 downloads, 0 subscriptions

About: Local alignment kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences.

Changes:

Initial Announcement on mloss.org.


About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files.

Changes:

Initial Announcement on mloss.org


About: Matlab code for semi-supervised regression and dimensionality reduction using Hessian energy.

Changes:

Initial Announcement on mloss.org.


Logo dysii Dynamic Systems Library 1.4.0

by lawmurray - December 17, 2008, 17:33:41 CET [ Project Homepage BibTeX Download ] 5369 views, 1338 downloads, 0 subscriptions

About: dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and [...]

Changes:

Initial Announcement on mloss.org.


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