About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. Changes:This release adds a number of new features, most important of which is a deep convolutional neural network version of the maxmargin object detection algorithm. This tool makes it very easy to create high quality object detectors. See http://dlib.net/dnn_mmod_ex.cpp.html for an introduction.

About: A Tool for Measuring String Similarity Changes:This release fixes the incorrect implementation of the bag distance.

About: Jatecs is an open source Java library focused on automatic text categorization. Changes:Initial Announcement on mloss.org.

About: A Tool for Embedding Strings in Vector Spaces Changes:Support for explicit selection of granularity added. Several minor bug fixes. We have reached 1.0

About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line. Changes:This release features the work of our 8 GSoC 2014 students [student; mentors]:
It also contains several cleanups and bugfixes: Features
Bugfixes
Cleanup and API Changes

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; Oneshot training for an entire regularization path; Continuous checkpointing; much more Changes:

About: MultiBoost is a multipurpose boosting package implemented in C++. It is based on the multiclass/multitask AdaBoost.MH algorithm [SchapireSinger, 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:

About: LIBOL is an opensource library with a family of stateoftheart online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification. Changes:In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows: • Add a template and guide for adding new algorithms; • Improve parameter settings and make documentation clear; • Improve documentation on data formats and key functions; • Amend the "OGD" function to use different loss types; • Fixed some name inconsistency and other minor bugs.

About: Support Vectors Machine library in .net with CUDA support. Library includes GPU SVM solver for kernels linear,RBF,ChiSquare and Exp ChiSquare which use NVIDIA CUDA technology. It allows for classification of feature rich sparse datasets through utilization of sparse matrix formats CSR, EllpackR or Sliced EllRT Changes:Initial Announcement on mloss.org.

About: Learns gradient boosted regression tree ensembles in parallel on shared memory or cluster systems Changes:Initial Announcement on mloss.org.

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org Changes:

About: redsvd is a library for solving several matrix decomposition (SVD, PCA, eigen value decomposition) redsvd can handle very large matrix efficiently, and optimized for a truncated SVD of sparse matrices. For example, redsvd can compute a truncated SVD with top 20 singular values for a 100K x 100K matrix with 10M nonzero entries in about two second. Changes:Initial Announcement on mloss.org.

About: A fast implementation of several stochastic gradient descent learners for classification, ranking, and ROC area optimization, suitable for large, sparse data sets. Includes Pegasos SVM, SGDSVM, PassiveAggressive Perceptron, Perceptron with Margins, Logistic Regression, and ROMMA. Commandline utility and API libraries are provided. Changes:Initial Announcement on mloss.org.

About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...] Changes:This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer. Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic). Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions. Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures). Unified automatic input checking via new static typing extending Python properties. Full support for recursive composition of larger components containing arbitrary statically typed state variables.

About: This software implements the Dirichlet Forest (DF) Prior within the Latent Dirichlet Allocation (LDA) model. When combined with LDA, the Dirichlet Forest Prior allows the user to encode domain knowledge (mustlinks and cannotlinks between words) into the prior on topicword multinomials. Changes:Initial Announcement on mloss.org.

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: OLaRankGreedy is an online solver of the dual formulation of support vector machines for sequence labeling using greedy inference. Changes:Initial Announcement on mloss.org.

About: OLaRank is an online solver of the dual formulation of support vector machines for sequence labeling using viterbi decoding. Changes:Initial Announcement on mloss.org.

About: BMRM is an open source, modular and scalable convex solver for many machine learning problems cast in the form of regularized risk minimization problem. Changes:Initial Announcement on mloss.org.

About: CoFiRank is a Collaborative Filtering system based on matrix factorization. CoFiRank is based on the idea that it is better to predict the relative order of preferences (ranking) instead of the absolute rating. Changes:Initial Announcement on mloss.org.
