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Logo NearOED 1.0

by gabobert - July 11, 2013, 16:54:12 CET [ Project Homepage BibTeX Download ] 1023 views, 270 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.

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Logo Ordinal Choquistic Regression 1.00

by AliFall - January 30, 2014, 15:42:34 CET [ BibTeX BibTeX for corresponding Paper Download ] 983 views, 230 downloads, 1 subscription

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

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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.

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Initial Announcement on mloss.org.


Logo ChaLearn Gesture Challenge Turtle Tamers 1.0

by konkey - March 17, 2013, 18:39:22 CET [ BibTeX Download ] 971 views, 405 downloads, 1 subscription

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

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Initial Announcement on mloss.org.


Logo AIDE 0.2

by khalili - January 3, 2014, 18:01:06 CET [ Project Homepage BibTeX Download ] 958 views, 222 downloads, 1 subscription

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

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Initial Announcement on mloss.org.


Logo HierLearning 1.0

by neville - March 2, 2014, 04:24:37 CET [ BibTeX BibTeX for corresponding Paper Download ] 951 views, 230 downloads, 1 subscription

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

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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.

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Logo r-cran-RPMM 1.06

by r-cran-robot - November 16, 2009, 00:00:00 CET [ Project Homepage BibTeX Download ] 919 views, 181 downloads, 0 subscriptions

About: Recursively Partitioned Mixture Model

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Logo Thalasso v0.2

by rherault - July 22, 2013, 15:33:59 CET [ Project Homepage BibTeX Download ] 916 views, 264 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.

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Logo RFD 1.0

by openpr_nlpr - April 28, 2014, 10:34:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 915 views, 211 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

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