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Logo NearOED 1.0

by gabobert - July 11, 2013, 16:54:12 CET [ Project Homepage BibTeX Download ] 1047 views, 277 downloads, 1 subscription

About: The toolbox from the paper Near-optimal Experimental Design for Model Selection in Systems Biology (Busetto et al. 2013, submitted) implemented in MATLAB.

Changes:

Initial Announcement on mloss.org.


About: Kaiye Wang, Ran He, Wei Wang, Liang Wang, Tiuniu Tan. Learning Coupled Feature Spaces for Cross-modal Matching. In ICCV, 2013.

Changes:

Initial Announcement on mloss.org.


Logo Ordinal Choquistic Regression 1.00

by AliFall - January 30, 2014, 15:42:34 CET [ BibTeX BibTeX for corresponding Paper Download ] 1028 views, 238 downloads, 1 subscription

About: "Ordinal Choquistic Regression" model using the maximum likelihood

Changes:

Initial Announcement on mloss.org.


Logo ChaLearn Gesture Challenge Turtle Tamers 1.0

by konkey - March 17, 2013, 18:39:22 CET [ BibTeX Download ] 1003 views, 412 downloads, 1 subscription

About: Soltion developed by team Turtle Tamers in the ChaLearn Gesture Challenge (http://www.kaggle.com/c/GestureChallenge2)

Changes:

Initial Announcement on mloss.org.


Logo HierLearning 1.0

by neville - March 2, 2014, 04:24:37 CET [ BibTeX BibTeX for corresponding Paper Download ] 1000 views, 239 downloads, 1 subscription

About: HierLearning is a C++11 implementation of a general-purpose, multi-agent, hierarchical reinforcement learning system for sequential decision problems.

Changes:

Initial Announcement on mloss.org.


Logo AIDE 0.2

by khalili - January 3, 2014, 18:01:06 CET [ Project Homepage BibTeX Download ] 998 views, 232 downloads, 1 subscription

About: AIDE (Automata Identification Engine) is a free open source tool for automata inference algorithms developed in C# .Net.

Changes:

Initial Announcement on mloss.org.


Logo RFD 1.0

by openpr_nlpr - April 28, 2014, 10:34:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 973 views, 228 downloads, 1 subscription

About: This is an unoptimized implementation of the RFD binary descriptor, which is published in the following paper. B. Fan, et al. Receptive Fields Selection for Binary Feature Description. IEEE Transaction on Image Processing, 2014. doi: http://dx.doi.org/10.1109/TIP.2014.2317981

Changes:

Initial Announcement on mloss.org.


Logo A Parallel LDA Learning Toolbox 1.0

by yanjianfeng - January 24, 2014, 11:48:07 CET [ BibTeX Download ] 960 views, 315 downloads, 1 subscription

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

Changes:

Fix some compiling errors.


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.


Logo Thalasso v0.2

by rherault - July 22, 2013, 15:33:59 CET [ Project Homepage BibTeX Download ] 940 views, 268 downloads, 1 subscription

About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|, where X and y are problem data and beta and lambda are variables.

Changes:

Initial Announcement on mloss.org.


Showing Items 501-510 of 543 on page 51 of 55: First Previous 46 47 48 49 50 51 52 53 54 55 Next