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About: A chatterbot that learns natural languages learning from imitation. Changes:Alpha 1 - Codename: Wendell Borton ("Bllluuhhhhh...!!") Short term memory greatly improved.
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About: Correlative Matrix Mapping (CMM) provides a supervised linear data mapping into a Euclidean subspace of given dimension. Applications include denoising, visualization, label-specific data preprocessing, and assessment of data attribute pairs relevant for the supervised mapping. Solving auto-association problems yields linear multidimensional scaling, similar to PCA, but usually with more faithful low-dimensional mappings. Changes:Tue Jul 5 14:40:03 CEST 2011 - Bugfixes and cleanups
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About: A Toolkit for Recursive Partytioning Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.838561
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About: The Kernel-Machine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines. Changes:Updated mloss entry (minor fixes).
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About: C-MixSim is an open source package written in C for simulating finite mixture models with Gaussian components. With a vast number of clustering algorithms, evaluating performance is important. C-MixSim provides an easy and convenient way of generating datasets from Gaussian mixture models with different levels of clustering complexity. C-MixSim is released under the GNU GPL license. Changes:Initial Announcement on mloss.org.
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About: A header-only C++ library for solving large scale eigenvalue problems Changes:
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About: Stochastic neighbor embedding originally aims at the reconstruction of given distance relations in a low-dimensional Euclidean space. This can be regarded as general approach to multi-dimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. The present implementation also allows for handling dissimilarity or score-induced neighborhood topologies and makes use of quasi 2nd order gradient-based (l-)BFGS optimization. Changes:
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About: Experiment Databases for Machine Learning is a large public database of machine learning experiments as well as a framework for producing similar databases for specific goals. It provides a way to [...] Changes:Initial Announcement on mloss.org.
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About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting). Changes:
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About: Likelihood-based Boosting for Generalized mixed models Changes:Fetched by r-cran-robot on 2013-04-01 00:00:05.366545
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About: MinorThird is a collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text. It was written primarily by William W. Cohen, a professor at [...] Changes:Initial Announcement on mloss.org.
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About: 3-layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM. Changes:Initial Announcement on mloss.org.
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About: BMRM is an open source, modular and scalable convex solver for many machine learning problems cast in the form of regularized risk minimization problem. Changes:Initial Announcement on mloss.org.
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About: Bayesian Additive Regression Trees Changes:Fetched by r-cran-robot on 2018-09-01 00:00:03.597464
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About: Torch is a statistical machine learning library written in C++ at IDIAP, Changes:Initial Announcement on mloss.org.
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About: Shrinkage Discriminant Analysis and CAT Score Variable Selection Changes:Fetched by r-cran-robot on 2012-02-01 00:00:11.559491
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About: Sequin is an open source sequence mining library written in C#. Changes:Sequin v1.1.0.0 released
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About: a dbms for resonating neural networks. Create and use different types of machine learning algorithms. Changes:AIML compatible (AIML files can be imported); new 'Grid channel' for developing board games; improved topics editor; new demo project: ALice (from AIML); lots of bug-fixes and speed improvements
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About: Jubatus is a general framework library for online and distributed machine learning. It currently supports classification, regression, clustering, recommendation, nearest neighbors, anomaly detection, and graph analysis. Loose model sharing provides higher scalability, better performance, and real-time capabilities, by combining online learning with distributed computations. Changes:0.5.0 add new supports for clustering and nearest neighbors. For more detail, see http://t.co/flMcTcYZVs
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