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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: 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:0.4.4 (Mon, Feb 2 2010) (Total: 144 commits) Primarily a bugfix release, probably the last in 0.4 series since development for 0.5 release is leaping forward.
o GNB implements Gaussian Naïve Bayes Classifier. o read_fsl_design() to read FSL FEAT design.fsf files (Contributed by Russell A. Poldrack). o SequenceStats to provide basic statistics on labels sequence (counter-balancing, autocorrelation). o New exceptions DegenerateInputError and FailedToTrainError to be thrown by classifiers primarily during training/testing. o Debug target STATMC to report on progress of Monte-Carlo sampling (during permutation testing).
o To get users prepared to 0.5 release, internally and in some examples/documentation, access to states and parameters is done via corresponding collections, not from the top level object (e.g. clf.states.predictions instead of soon-to-be-deprecated clf.predictions). That should lead also to improved performance. o Adopted copy.py from python2.6 (support Ellipsis as well). ed (38 BF commits): o GLM output does not depend on the enabled states any more. o Variety of docstrings fixed and/or improved. o Do not derive NaN scaling for SVM’s C whenever data is degenerate (lead to never finishing SVM training). o sg : + KRR is optional now – avoids crashing if KRR is not available.
o Python 2.4 compatibility issues: kNN and IFS
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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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About: Very simple code for training SVMs in the primal. Works particularly well on sparse linear problems. In the non-linear case the entire kernel matrix needs to be computed, so for large problems it is [...] Changes:Initial Announcement on mloss.org.
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About: The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be [...] Changes:Initial Announcement on mloss.org.
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