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About: MA-sLDAr is a C++ implementation of the supervised topic models with response variables provided by multiple annotators with different levels of expertise. Changes:Initial Announcement on mloss.org.
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About: Implementation of the multi-assignment clustering method for Boolean vectors. Changes:new bib added
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About: The Multiagent decision process (MADP) Toolbox is a free C++ software toolbox for scientific research in decision-theoretic planning and learning in multiagent systems. Changes:-Includes freshly written spirit parser for .pomdp files. -Includes new code for pruning POMDP vectors; obviates dependence on Cassandra's code and old LP solve version. -Includes new factor graph solution code -Generalized firefighting CGBG domain added -Simulation class for Factored Dec-POMDPs and TOI Dec-MDPs -Approximate BG clustering methods and kGMAA with clustering.
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About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine. Changes:Major changes :
Minor fixes:
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About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition Changes:
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About: This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008. Changes:Initial Announcement on mloss.org.
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About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more Changes:This version comes with Distributed and Mobile Examples
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About: MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms. Changes:Mostly bug fixes. For details see: https://github.com/zenogantner/MyMediaLite/blob/master/doc/Changes
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About: This program is a C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. The program uses the multinomial event model for representation, the maximum likelihood estimate with a Laplace smoothing technique for learning parameters. A sparse-data structure is defined to represent the feature vector in the program to seek higher computational speed. Changes:Initial Announcement on mloss.org.
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About: OpenPR-NBEM is an C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. OpenPR-NBEM uses the multinomial event model for representation. The maximum likelihood estimate is used for supervised learning, and the expectation-maximization estimate is used for semi-supervised and un-supervised learning. Changes:Initial Announcement on mloss.org.
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