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Showing Items 151-160 of 619 on page 16 of 62: First Previous 11 12 13 14 15 16 17 18 19 20 21 Next Last

Logo GPDT Gradient Projection Decomposition Technique 1.01

by sezaza - December 21, 2007, 20:10:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9598 views, 1791 downloads, 1 subscription

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About: This is a C++ software designed to train large-scale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...]

Changes:

Initial Announcement on mloss.org.


Logo Dependency modeling toolbox 0.2

by lml - April 30, 2010, 14:38:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9554 views, 1490 downloads, 1 subscription

About: Investigation of dependencies between multiple data sources allows the discovery of regularities and interactions that are not seen in individual data sets. The demand for such methods is increasing with the availability and size of co-occurring observations in computational biology, open data initiatives, and in other domains. We provide practical, open access implementations of general-purpose algorithms that help to realize the full potential of these information sources.

Changes:

Three independent modules (drCCA, pint, MultiWayCCA) have been added.


Logo VLFeat 0.9.16

by andreavedaldi - October 5, 2012, 18:44:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9520 views, 1753 downloads, 1 subscription

About: The VLFeat open source library implements popular computer vision algorithms including affine covariant feature detectors, HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.16.

Changes:

VLFeat 0.9.16: Added VL_COVDET() (covariant feature detectors). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme.

VLFeat 0.9.15: Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly).


Logo r-cran-rattle 2.6.26

by r-cran-robot - March 16, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 9401 views, 2075 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 Aleph 0.6

by jiria - January 12, 2009, 20:52:12 CET [ Project Homepage BibTeX Download ] 9376 views, 2618 downloads, 1 subscription

About: Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.

Changes:

Initial Announcement on mloss.org.


Logo Graph kernel based on iterative graph similarity and optimal assignments 2008-01-15

by mrupp - September 22, 2008, 13:42:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9338 views, 1627 downloads, 2 subscriptions

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About: Java package implementing a kernel for (molecular) graphs based on iterative graph similarity and optimal assignments.

Changes:

Initial Announcement on mloss.org.


About: Matlab code for performing variational inference in the Indian Buffet Process with a linear-Gaussian likelihood model.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-mvpart 1.6-0

by r-cran-robot - February 19, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 9184 views, 1867 downloads, 1 subscription

About: Multivariate partitioning

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.387032


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 RLS2 MATLAB Toolbox 0.7

by posaune - March 31, 2010, 20:37:11 CET [ Project Homepage BibTeX Download ] 9159 views, 1961 downloads, 1 subscription

About: RLS2 is an instance of multiple kernel learning algorithm to simultaneously learn a regularized predictor and the kernel function. RLS2LIN is a version of RLS2 specialized to linear kernels on each feature. The package contains a set of scripts that implements RLS2 and RLS2LIN, together with a Graphic User Interface to load data, perform training, validation, and plot results.

Changes:
  • New kernel functions (rbfall, rbfsingle, polyall, polysingle)
  • Improved interface for pre-processing operations
  • The interface now allows to disable bias
  • Fixed bugs in parameter passing (thanks to Andrea Schirru)

Showing Items 151-160 of 619 on page 16 of 62: First Previous 11 12 13 14 15 16 17 18 19 20 21 Next Last