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About: Regularization for semiparametric additive hazards regression Changes:Fetched by rcranrobot on 20160601 00:00:04.012954

About: The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from largescale data. Changes:Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.

About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression. Changes:Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.

About: Toolbox for circular statistics with Matlab (The Mathworks). Changes:Some bugfixes.

About: Bayesian Additive Regression Trees Changes:Fetched by rcranrobot on 20160601 00:00:04.185346

About: This is a C++ software designed to train largescale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...] Changes:Initial Announcement on mloss.org.

About: JNCC2 is the opensource implementation of the Naive Credal Classifier2 (NCC2), i.e., an extension of Naive Bayes towards imprecise probabilities, designed to deliver robust classifications even on [...] Changes:Initial Announcement on mloss.org.

About: Logic Regression Changes:Fetched by rcranrobot on 20130401 00:00:06.139495

About: LibSGDQN proposes an implementation of SGDQN, a carefully designed quasiNewton stochastic gradient descent solver for linear SVMs. Changes:small bug fix (thx nicolas ;)

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly nonlinear and nonconvex functions, using the CMAES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability. Changes:This is a major release, with several novelties, improvements and fixes, among which:
