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Showing Items 141-150 of 535 on page 15 of 54: First Previous 10 11 12 13 14 15 16 17 18 19 20 Next Last

Logo GPUML GPUs for kernel machines 4

by balajivasan - February 26, 2010, 18:12:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4796 views, 816 downloads, 1 subscription

About: GPUML is a library that provides a C/C++ and MATLAB interface for speeding up the computation of the weighted kernel summation and kernel matrix construction on GPU. These computations occur commonly in several machine learning algorithms like kernel density estimation, kernel regression, kernel PCA, etc.

Changes:

Initial Announcement on mloss.org.


Logo GradMC 2.00

by tur - April 14, 2014, 15:48:48 CET [ BibTeX Download ] 1375 views, 482 downloads, 1 subscription

About: GradMC is an algorithm for MR motion artifact removal implemented in Matlab

Changes:

Added support for multi-rigid motion correction.


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 ] 7407 views, 1250 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.


Logo Graph Learning Package 0.1

by hiroto - May 4, 2009, 17:07:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6460 views, 1202 downloads, 0 subscriptions

About: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features.

Changes:

Initial Announcement on mloss.org.


Logo GraphDemo 1.0

by ule - November 27, 2007, 20:11:21 CET [ Project Homepage BibTeX Download ] 3775 views, 1067 downloads, 0 subscriptions

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About: The GraphDemo provides Matlab GUIs to explore similarity graphs and their use in machine learning. It aims to highlight the behavior of different kinds of similarity graphs and to demonstrate their [...]

Changes:

Initial Announcement on mloss.org.


Logo Graphical Models and Conditional Random Fields Toolbox 2

by jdomke - January 5, 2012, 15:38:20 CET [ Project Homepage BibTeX Download ] 2071 views, 485 downloads, 1 subscription

About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the high-treewidth setting.

Changes:

Initial Announcement on mloss.org.


Logo GraphLab v1-1908

by dannybickson - November 22, 2011, 12:50:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3874 views, 684 downloads, 1 subscription

About: Multicore/distributed large scale machine learning framework.

Changes:

Update version.


Logo GritBot 2.01

by zenog - September 2, 2011, 14:56:26 CET [ Project Homepage BibTeX Download ] 2097 views, 521 downloads, 1 subscription

About: GritBot is an data cleaning and outlier/anomaly detection program.

Changes:

Initial Announcement on mloss.org.


Logo gWT graph indexing wavelet tree 1.0.0

by ytabei - May 12, 2011, 23:01:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2876 views, 544 downloads, 1 subscription

About: Software for graph similarity search for massive graph databases

Changes:

Initial Announcement on mloss.org.


About: Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explore their relation is still an open problem. In this paper, we develop a half-quadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an L1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an L1-regularized error detection method by learning from uncorrupted data iteratively. We also show that the L1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings.

Changes:

Initial Announcement on mloss.org.


Showing Items 141-150 of 535 on page 15 of 54: First Previous 10 11 12 13 14 15 16 17 18 19 20 Next Last