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About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.
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About: SAMOA is a platform for mining big data streams. It is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. Changes:Initial Announcement on mloss.org.
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About: A Theano framework for building and training neural networks Changes:Initial Announcement on mloss.org.
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About: A Machine Learning framework for Objective-C and Swift (OS X / iOS) Changes:Initial Announcement on mloss.org.
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About: FsAlg is a linear algebra library that supports generic types. Changes:Initial Announcement on mloss.org.
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About: Easy differential privacy Changes:Initial Announcement on mloss.org.
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About: Software for Automatic Construction and Inference of DBNs Based on Mathematical Models Changes:Initial Announcement on mloss.org.
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About: Q. Dong, Two-dimensional relaxed representation, Neurocomputing, 121:248-253, 2013, http://dx.doi.org/10.1016/j.neucom.2013.04.044 Changes:Initial Announcement on mloss.org.
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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.
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About: This code is developed based on Uriel Roque's active set algorithm for the linear least squares problem with nonnegative variables in: Portugal, L.; Judice, J.; and Vicente, L. 1994. A comparison of block pivoting and interior-point algorithms for linear least squares problems with nonnegative variables. Mathematics of Computation 63(208):625-643.Ran He, Wei-Shi Zheng and Baogang Hu, "Maximum Correntropy Criterion for Robust Face Recognition," IEEE TPAMI, in press, 2011. Changes:Initial Announcement on mloss.org.
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About: Urheen is a toolkit for Chinese word segmentation, Chinese pos tagging, English tokenize, and English pos tagging. The Chinese word segmentation and pos tagging modules are trained with the Chinese Tree Bank 7.0. The English pos tagging module is trained with the WSJ English treebank(02-23). Changes:Initial Announcement on mloss.org.
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About: This is a library for solving nu-SVM by using Wolfe's minimum norm point algorithm. You can solve binary classification problem. Changes:Initial Announcement on mloss.org.
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About: glyph is a python 3 library based on deap providing abstraction layers for symbolic regression problems. Changes:Initial Announcement on mloss.org.
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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.
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About: Scilab Pattern Recognition Toolbox is a toolbox developed for Scilab software, and is used in pattern recognition, machine learning and the related field. It is developed for the purpose of education and research. Changes:Initial Announcement on mloss.org.
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About: "Ordinal Choquistic Regression" model using the maximum likelihood Changes:Initial Announcement on mloss.org.
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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.
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About: Operator Discretization Library (ODL) is a Python library that enables research in inverse problems on realistic or real data. Changes:Initial Announcement on mloss.org.
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About: Collection of algorithms for Gaussian Processes. Regression, Classification, Multi task, Multi output, Hierarchical, Sparse Changes:Initial Announcement on mloss.org.
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About: This program implements a novel robust sparse representation method, called the two-stage sparse representation (TSR), for robust recognition on a large-scale database. Based on the divide and conquer strategy, TSR divides the procedure of robust recognition into outlier detection stage and recognition stage. The extensive numerical experiments on several public databases demonstrate that the proposed TSR approach generally obtains better classification accuracy than the state-of-the-art Sparse Representation Classification (SRC). At the same time, by using the TSR, a significant reduction of computational cost is reached by over fifty times in comparison with the SRC, which enables the TSR to be deployed more suitably for large-scale dataset. Changes:Initial Announcement on mloss.org.
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