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About: Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. Support element-wise expression expand in high performance. Code once, run smoothly on both GPU and CPU Changes:Initial Announcement on mloss.org.
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About: CXXNET (spelled as: C plus plus net) is a neural network toolkit build on mshadow(https://github.com/tqchen/mshadow). It is yet another implementation of (convolutional) neural network. It is in C++, with about 1000 lines of network layer implementations, easily configuration via config file, and can get the state of art performance. Changes:Initial Announcement on mloss.org.
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About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction. Changes:Initial Announcement on mloss.org.
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About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license. Changes:New version November 2013
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About: SAMOA is a platform for mining big data streams. It is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. Changes:Initial Announcement on mloss.org.
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About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine. Changes:Major changes :
Minor fixes:
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About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks. Changes:The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:
The API and ABI have undergone significant changes, many of which are due to the transition to C++11.
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About: Log-linear analysis for high-dimensional data Changes:Initial Announcement on mloss.org.
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About: The package "fastclime" provides a method of recover the precision matrix efficiently by applying parametric simplex method. The computation is based on a linear optimization solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method. Changes:Initial Announcement on mloss.org.
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About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications. Changes:
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About: HierLearning is a C++11 implementation of a general-purpose, multi-agent, hierarchical reinforcement learning system for sequential decision problems. Changes:Initial Announcement on mloss.org.
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About: DAL is an efficient and flexibible MATLAB toolbox for sparse/low-rank learning/reconstruction based on the dual augmented Lagrangian method. Changes:
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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: The package computes the optimal parameters for the Choquet kernel 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: "Ordinal Choquistic Regression" model using the maximum likelihood Changes:Initial Announcement on mloss.org.
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About: We introduces PLL, a parallel LDA learning toolbox for big topic modeling. Changes:Fix some compiling errors.
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About: DRVQ is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. Changes:Initial Announcement on mloss.org.
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About: AIDE (Automata Identification Engine) is a free open source tool for automata inference algorithms developed in C# .Net. Changes:Initial Announcement on mloss.org.
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