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: 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: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods. Changes:Additions and improvements from ELKI 0.7.0 to 0.7.1: Algorithm additions:
Important bug fixes:
UI improvements:
Smaller changes:
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About: Hubness-aware Machine Learning for High-dimensional Data Changes:
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About: DDN learns and visualize differential dependency networks from condition-specific data. Changes:Initial Announcement on mloss.org.
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About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning. Changes:New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)
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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:The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL. Changed the BibTeX reference to the paper recently published in JMLR MLOSS.
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About: Divvy is a Mac OS X application for performing dimensionality reduction, clustering, and visualization. Changes:Initial Announcement on mloss.org.
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About: MLPlot is a lightweight plotting library written in Java. Changes:Initial Announcement on mloss.org.
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About: The Delay vector variance (DVV) method uses predictability of the signal in phase space to characterize the time series. Using the surrogate data methodology, so called DVV plots and DVV scatter [...] Changes:Initial Announcement on mloss.org.
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