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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: Code for Calibrated AdaMEC for binary cost-sensitive classification. The method is just AdaBoost that properly calibrates its probability estimates and uses a cost-sensitive (i.e. risk-minimizing) decision threshold to classify new data. Changes:Updated license information
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About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms. Changes:
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About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, unsupervised feature learning and clustering tasks. Changes:
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About: A library of scalable Bayesian generalised linear models with fancy features Changes:
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About: The scikit-learn project is a machine learning library in Python. Changes:Update for 0.18 .1
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About: RLScore - regularized least-squares machine learning algorithms package 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, cross quantities and kernels on distributions. 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: TBEEF, a doubly ensemble framework for recommendation and prediction problems. Changes:Included the final technical report.
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About: Python toolbox for manifold optimization with support for automatic differentiation Changes:Initial Announcement on mloss.org.
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About: A Python based library for running experiments with Deep Learning and Ensembles on GPUs. Changes:Initial Announcement on mloss.org.
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About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more. Changes:New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.
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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 bug in save/restore. -Fixed bug in initial design.
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About: Easily prototype WEKA classifiers and filters using Python scripts. Changes:0.3.0
0.2.1
0.2.0
0.1.1
0.1.0
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About: An open-source Python toolbox to analyze mobile phone metadata. Changes:Initial Announcement on mloss.org.
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About: This algorithm is described in Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval. See https://github.com/zhaofang0627/cuda-convnet-for-hashing 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: Variational Bayesian inference tools for Python Changes:
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About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions. Changes:This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.
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About: libnabo is a fast K Nearset Neighbor library for low-dimensional spaces. Changes:
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