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About: This package implements Ideal PCA in MATLAB. Ideal PCA is a (cross-)kernel based feature extraction algorithm which is (a) a faster alternative to kernel PCA and (b) a method to learn data manifold certifying features. Changes:Initial Announcement on mloss.org.
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About: CN24 is a complete semantic segmentation framework using fully convolutional networks. Changes:Initial Announcement on mloss.org.
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About: The Chestnut Machine Learning Library is a suite of machine learning algorithms written in Python with some code written in C for efficiency. Most algorithms are called with a simple, functional API [...] Changes:Initial Announcement on mloss.org.
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About: Estimates statistical significance of association between variables and their principal components (PCs). Changes:Initial Announcement on mloss.org.
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About: An open-source C# market-basket synthetic data generator, capable of creating transactions, sequences and taxonomies, based on the IBM Quest version. Written to address the maintainability and portability problems of the original, feedback, fixes and extensions are encouraged! Changes:Initial Announcement on mloss.org.
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About: A toolkit for hyperparameter optimization for machine learning algorithms. Changes:Initial Announcement on mloss.org.
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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: Piqle (Platform for Implementing Q-Learning Experiments) is a Java framework for fast design, prototyping and test of reinforcement learning experiments (RL). By clearly separating algorithms and problems, it allows users to focus on either part of the RL paradigm:designing new algorithms or implementing new problems. Piqle implements many classical RL algorithms, making their parameters easily tunable. At this time, 13 problems are implemented, several with one or more variants. The user's manual explains in detail how to code a new problem. Written in Java, Piqle is as platform-independent as Java itself. Its components can easily be embedded as part of complex implementations, like robotics or decision making. Changes:Initial Announcement on mloss.org.
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About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation. 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: aiParts implements the High-Hope technique - options have models of emotions which affect and are affected by repeated attempts to solve a multi-decision problem. C++ classes for AI development. Changes:Initial Announcement on mloss.org.
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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: Kaiye Wang, Ran He, Wei Wang, Liang Wang, Tiuniu Tan. Learning Coupled Feature Spaces for Cross-modal Matching. In ICCV, 2013. Changes:Initial Announcement on mloss.org.
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About: Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.) Changes:Initial Announcement on mloss.org.
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About: Baycomp is a library for Bayesian comparison of classifiers, as a better alternative for null-hypothesis significance testing. Changes:Initial Announcement on mloss.org.
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About: Eblearn is an object-oriented C++ library that implements various Changes:Initial Announcement on mloss.org.
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About: Learns dynamic network changes across conditions and visualize the results in Cytoscape. Changes:Initial Announcement on mloss.org.
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About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scala-lang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline. Changes:Initial Announcement on mloss.org.
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About: This is a set of MATLAB(R) functions and MEX files which I wrote to make working with this system somewhat bearable. They allow to call BLAS and LAPACK functions, which do very efficient dense [...] Changes:Initial Announcement on mloss.org.
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