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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: 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 descriptive and programming language independent format and API for the simplified configuration, documentation, and design of computer experiments. Changes:Initial Announcement on mloss.org.
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About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making. Changes:
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About: Universal Python-written numerical optimization toolbox. Problems: NLP, LP, QP, NSP, MILP, LSP, LLSP, MMP, GLP, SLE, MOP etc; general logical constraints, categorical variables, automatic differentiation, interval analysis, many other goodies Changes:http://openopt.org/Changelog
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About: The scikit-learn aims to provide state of the art standard machine learning algorithms in Python. Changes:Update for 0.13.1
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About: libnabo is a fast K Nearset Neighbor library for low-dimensional spaces. Changes:
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About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...] Changes:Added multi-target and multi-label learning, neural networks, Earth (MARS), PLS, and a faster tree induces for use in random forests; reorganization of module hierarchy; (weakly supported) Qwt has been replaced with a homemade module; networkx is used instead of a (weak) homemade structures for graphs; documentation has been moved to .rst, with a lot of it written anew or heavily redacted; improved system for registration of add-ons.
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About: peewit provides services for programming, running and result examination of machine learning experiments. It does not include any ML algorithms, has no GUI, and presumes certain uniformity of the experimental layout. But it does not make assumptions on the type of task under study. Changes:switched to python-3
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About: MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms. Changes:Important changes:
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About: Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. Changes:Release 1.1.2
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About: Python Framework for Vector Space Modelling that can handle unlimited datasets (streamed input, online algorithms work incrementally in constant memory). Changes:
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About: Python Machine Learning Toolkit Changes:Added LASSO (using coordinate descent optimization). Made SVM classification (learning and applying) much faster: 2.5x speedup on yeast UCI dataset.
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About: MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others. Changes:What's new in version 3.3?
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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields. Changes:Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).
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About: A general purpose library to process and predict sequences of elements using echo state networks. Changes:Initial Announcement on mloss.org.
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About: "Pattern" is a web mining module for Python. It bundles tools for data retrieval, text analysis, clustering and classification, and data visualization. Changes:
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About: The OrGanic Environment for Reservoir computing (Oger) toolbox is a Python toolbox for rapidly building, training and evaluating modular learning architectures on large datasets. Changes:Initial Announcement on mloss.org.
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About: This package contains a python and a matlab implementation of the most widely used algorithms for multi-armed bandit problems. The purpose of this package is to provide simple environments for comparison and numerical evaluation of policies. Changes:Initial Announcement on mloss.org.
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About: Nimfa is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. Changes:Initial Announcement on mloss.org.
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