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About: Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. Changes:Initial Announcement on mloss.org.
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About: Bayesian state-space modelling and inference on high-performance computer hardware. Changes:Initial Announcement on mloss.org.
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About: Epistatic miniarray profiles (E-MAPs) are a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used - a simple weighted nearest neighbors, and a local least squares based regression. Changes:Initial Announcement on mloss.org.
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About: A Sortware for All Pairs Similarity Search Changes:Initial Announcement on mloss.org.
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About: BudgetedSVM is an open-source C++ toolbox for scalable non-linear classification. The toolbox can be seen as a missing link between LibLinear and LibSVM, combining the efficiency of linear with the accuracy of kernel SVM. We provide an Application Programming Interface for efficient training and testing of non-linear classifiers, supported by data structures designed for handling data which cannot fit in memory. We also provide command-line and Matlab interfaces, providing users with an efficient, easy-to-use tool for large-scale non-linear classification. Changes:Changed license from LGPL v3 to Modified BSD.
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About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the high-treewidth setting. Changes:Initial Announcement on mloss.org.
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About: LOFS is a software toolbox for online streaming feature selection Changes:Initial Announcement on mloss.org.
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About: MLlib provides a distributed machine learning (ML) library to address the growing need for scalable ML. MLlib is developed in Spark (http://spark.incubator.apache.org/), a cluster computing system designed for iterative computation. Moreover, it is a component of a larger system called MLbase (www.mlbase.org) that aims to provide user-friendly distributed ML functionality both for ML researchers and domain experts. MLlib currently consists of scalable implementations of algorithms for classification, regression, collaborative filtering and clustering. Changes:Initial Announcement on mloss.org.
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About: Toolkit for parametric and nonparametric regression and classification. Changes:Initial Announcement on mloss.org.
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About: ClowdFlows is a web based platform for service oriented data mining publicly available at http://clowdflows.org . A web based interface allows users to construct data mining workflows that are hosted on the web and can be (if allowed by the author) accessed by anyone by following a URL of the workflow. Changes:Initial Announcement on mloss.org.
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About: Soltion developed by team Turtle Tamers in the ChaLearn Gesture Challenge (http://www.kaggle.com/c/GestureChallenge2) Changes:Initial Announcement on mloss.org.
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About: revised version of BACOM Changes:Initial Announcement on mloss.org.
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About: GPU-accelerated java deep neural networks Changes:Initial Announcement on mloss.org.
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About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.
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About: RLScore - regularized least-squares machine learning algorithms package Changes:Initial Announcement on mloss.org.
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About: A streaming inference and query engine for the Cross-Categorization model of tabular data. Changes:Initial Announcement on mloss.org.
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About: The original Random Forests implementation by Breiman and Cutler. Changes:Initial Announcement on mloss.org.
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About: Embarrassingly Parallel Array Computing: EPAC is a machine learning workflow builder. Changes:Initial Announcement on mloss.org.
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About: DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes implementing predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows. Changes:Initial Announcement on mloss.org.
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