About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini. Changes:
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About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis. Changes:Initial Announcement on mloss.org.
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About: Distributed optimization: Support Vector Machines and LASSO regression on distributed data Changes:Initial Upload
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About: The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library that has been specifically designed for real-time gesture recognition. It features a large number of machine-learning algorithms for both classification and regression in addition to a wide range of supporting algorithms for pre-processing, feature extraction and dataset management. The GRT has been designed for real-time gesture recognition, but it can also be applied to more general machine-learning tasks. Changes:Added Decision Tree and Random Forests.
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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: The Kernel-Machine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines. Changes:Updated mloss entry (minor fixes).
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About: Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, [...] Changes:
This release aggregates all the changes occurred between official
releases in 0.4 series and various snapshot releases (in 0.5 and 0.6
series). To get better overview of high level changes see
:ref:
Also adapts changes from 0.4.6 and 0.4.7 (see corresponding changelogs).
This is a special release, because it has never seen the general public.
A summary of fundamental changes introduced in this development version
can be seen in the :ref: Most notably, this version was to first to come with a comprehensive two-day workshop/tutorial.
A bugfix release
A bugfix release
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About: The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from large-scale data. Changes:Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.
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About: This software is designed for learning translation invariant kernels for classification with support vector machines. Changes:Initial Announcement on mloss.org.
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About: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class [...] Changes:Initial Announcement on mloss.org.
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About: SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides various machine learning and computational intelligence techniques. Changes:
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About: LibSGDQN proposes an implementation of SGD-QN, a carefully designed quasi-Newton stochastic gradient descent solver for linear SVMs. Changes:small bug fix (thx nicolas ;)
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About: The Easysvm package provides a set of tools based on the Shogun toolbox allowing to train and test SVMs in a simple way. Changes:Fixes for shogun 0.7.3.
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About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, L1-loss linear SVM, and multi-class SVM Changes:Initial Announcement on mloss.org.
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About: LaRank is an online solver for multiclass Support Vector Machines. Changes:Initial Announcement on mloss.org.
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About: SVM Toolbox fully written in Matlab (even the QP solver). Features : SVM, MultiClassSVM, One-Class, SV Regression, AUC-SVM and Rankboost, 1-norm SVM, Regularization Networks, Kernel Basis Pursuit [...] Changes:Initial Announcement on mloss.org.
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About: PyML is an interactive object oriented framework for machine learning in python with a focus on kernel methods. Changes:Initial Announcement on mloss.org.
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About: BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes three methods Changes:Initial Announcement on mloss.org.
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About: This is a C++ software designed to train large-scale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...] Changes:Initial Announcement on mloss.org.
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