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About: pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem. Changes:pycobra is further pep8 compliant, has improved tests and more plotting options.
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About: The Advanced Data mining And Machine learning System (ADAMS) is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Changes:Some highlights:
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About: Operator Discretization Library (ODL) is a Python library that enables research in inverse problems on realistic or real data. Changes:Initial Announcement on mloss.org.
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About: A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation Changes:Release 0.7.0
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About: A Spark package implementing algorithms for learning from crowdsourced big data. Changes:Changes: - Minor improvements in code and documentation
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About: A method to optimize the hyperparameters for machine learning methods implemented in Scikit-learn based on Derivative Free Optimization Changes:Initial Announcement on mloss.org.
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About: Obandit is an Ocaml module for multi-armed bandits. It supports the EXP, UCB and Epsilon-greedy family of algorithms. Changes:Initial Announcement on mloss.org.
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About: This is a tool for retrieving nearest neighbors and clustering of large categorical data sets represented in transactional form. The clustering is achieved via a locality-sensitive hashing of categorical datasets for speed and scalability. Changes:Initial Announcement on mloss.org.
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About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details. Changes:For a complete list of changes, please see the full release notes at the release details page at: https://github.com/accord-net/framework/releases/tag/v3.8.0
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About: The bufferkdtree package is a Python library that aims at accelerating nearest neighbor computations using both k-d trees and modern many-core devices such as graphics processing units (GPUs). Changes:Initial Announcement on mloss.org.
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About: Effect Displays for Linear, Generalized Linear, and Other Models Changes:Fetched by r-cran-robot on 2018-01-01 00:00:07.810965
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About: A Java framework for statistical analysis and classification of biological sequences Changes:New classes and packages:
New features and improvements:
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About: Tool aimed at helping remedy the reproducibility problem, specifically in the statistical and data wrangling aspects. Changes:Initial Announcement on mloss.org.
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About: A novel method to create parallel coordinates plots on large data sets without causing a "black screen" problem. Changes:Initial Announcement on mloss.org.
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About: A non-iterative, incremental and hyperparameter-free learning method for one-layer feedforward neural networks without hidden layers. This method efficiently obtains the optimal parameters of the network, regardless of whether the data contains a greater number of samples than variables or vice versa. It does this by using a square loss function that measures errors before the output activation functions and scales them by the slope of these functions at each data point. The outcome is a system of linear equations that obtain the network's weights and that is further transformed using Singular Value Decomposition. Changes:Initial Announcement on mloss.org.
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About: Classification, Regression and Feature Evaluation Changes:Fetched by r-cran-robot on 2018-01-01 00:00:07.164852
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About: A non-iterative learning method for one-layer (no hidden layer) neural networks, where the weights can be calculated in a closed-form manner, thereby avoiding low convergence rate and also hyperparameter tuning. The proposed learning method, LANN-SVD in short, presents a good computational efficiency for large-scale data analytic. Changes:Initial Announcement on mloss.org.
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About: An open-source framework for benchmarking of feature selection algorithms and cost functions. Changes:Initial Announcement on mloss.org.
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About: A generalized version of spectral clustering using the graph p-Laplacian. Changes:various internal optimizations
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About: Hyperstream is a large-scale, flexible and robust software package for processing streaming data. Changes:python 3 support; new API; bug fixes and enhancements
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