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About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python. Changes:-Fixed bugs. -Improved and extended documentation. -Extended and simplified API accross platforms. -Extended functionality (new surrogate functions, new priors, new kernels, new criteria). -Improved modularity of the optimization process to allow plotting and debugging of intermediate steps. -Added more demos and examples.
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About: Somoclu is a cluster-oriented implementation of self-organizing maps. It relies on MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Changes:Initial Announcement on mloss.org.
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About: Robust learning of Bayesian Networks Changes:Initial Announcement on mloss.org.
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About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures and cross quantities. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. Changes:
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About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure Changes:Updated to version 1.0
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About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA. Changes:minor changes
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About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier. Changes:
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About: A scalable, fast C++ machine learning library, with emphasis on usability. Changes:Speedups of cover tree traversers; addition of rank-approximate nearest neighbor (RANN); addition of fast exact max-kernel search (FastMKS); fix for EM covariance estimation; more parameters for GMM estimation; force GMM and GaussianDistribution covariance matrices to be positive definite during training; add a tolerance parameter to the Baum-Welch algorithm for HMM training; fix for compilation with clang; fix for k-furthest neighbor search.
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About: Multivariate Adaptive Regression Spline Models Changes:Fetched by r-cran-robot on 2013-05-01 00:00:06.060849
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About: Cox models by likelihood based boosting for a single survival endpoint or competing risks Changes:Fetched by r-cran-robot on 2013-05-01 00:00:05.557880
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