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Logo Metropolis Hastings algorithm 1.0.0

by openpr_nlpr - December 2, 2011, 05:43:20 CET [ Project Homepage BibTeX Download ] 1833 views, 476 downloads, 1 subscription

About: Metropolis-Hastings alogrithm is a Markov chain Monte Carlo method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. Thi sequence can be used to approximate the distribution.

Changes:

Initial Announcement on mloss.org.


Logo factorie 1.0.0-M7

by apassos - October 7, 2013, 23:10:37 CET [ Project Homepage BibTeX Download ] 2198 views, 469 downloads, 1 subscription

About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scala-lang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline.

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.


Logo Nonparametric Sparse Factor Analysis 1

by davidknowles - July 26, 2013, 01:02:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2180 views, 463 downloads, 1 subscription

About: This is the core MCMC sampler for the nonparametric sparse factor analysis model presented in David A. Knowles and Zoubin Ghahramani (2011). Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling. Annals of Applied Statistics

Changes:

Initial Announcement on mloss.org.


Logo Semi Stochastic Gradient Descent 1.0

by konkey - July 9, 2014, 04:28:47 CET [ BibTeX BibTeX for corresponding Paper Download ] 1706 views, 456 downloads, 1 subscription

About: Efficient implementation of Semi-Stochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2-regularized).

Changes:

Initial Announcement on mloss.org.


Logo Calculate Normalized Information Measures 1.0.0

by openpr_nlpr - December 2, 2011, 04:35:32 CET [ Project Homepage BibTeX Download ] 1461 views, 456 downloads, 1 subscription

About: The toolbox is to calculate normalized information measures from a given m by (m+1) confusion matrix for objective evaluations of an abstaining classifier. It includes total 24 normalized information measures based on three groups of definitions, that is, mutual information, information divergence, and cross entropy.

Changes:

Initial Announcement on mloss.org.


Logo Layer Based Dependency Parser 1.0.0

by openpr_nlpr - December 2, 2011, 04:51:23 CET [ Project Homepage BibTeX Download ] 1413 views, 452 downloads, 1 subscription

About: LDPar is an efficient data-driven dependency parser. You can train your own parsing model on treebank data and parse new data using the induced model.

Changes:

Initial Announcement on mloss.org.


Logo AlCoCoMa 1.0

by kiraly - November 8, 2013, 09:38:07 CET [ BibTeX BibTeX for corresponding Paper Download ] 1701 views, 451 downloads, 1 subscription

About: ALgebraic COmbinatorial COmpletion of MAtrices. A collection of algorithms to impute or denoise single entries in an incomplete rank one matrix, to determine for which entries this is possible with any algorithm, and to provide algorithm-independent error estimates. Includes demo scripts.

Changes:

Initial Announcement on mloss.org.


Logo Pattern Recognition for Neuroimaging Toolbox v1.1

by chrisp - September 16, 2013, 23:19:56 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2268 views, 449 downloads, 1 subscription

About: PRoNTo is freely available software and aims to facilitate the interaction between the neuroimaging and machine learning communities. The toolbox is based on pattern recognition techniques for the analysis of neuroimaging data. PRoNTo supports the analysis of all image modalities as long as they are NIfTI format files. However, only the following modalites have been tested for version 1.1: sMRI, fMRI, PET, FA (fractional anisotropy) and Beta (GLM coefficients) images.

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 ] 1588 views, 440 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.


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