About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

This release adds a bunch of new image processing routines as well as many minor usability improvements and bug fixes.

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

• Conditional Shannon entropy estimation: added.

• Conditional Shannon mutual information estimation: included.

About: A fast, flexible C++ machine learning library, with bindings to other languages.

Released June 8th, 2018.

• Documentation generation fixes for Python bindings (#1421).
• Fix build error for man pages if command-line bindings are not being built (#1424).
• Add shuffle parameter and Shuffle() method to KFoldCV (#1412). This will shuffle the data when the object is constructed, or when Shuffle() is called.
• Added neural network layers: AtrousConvolution (#1390), Embedding (#1401), and LayerNorm (layer normalization) (#1389).
• Add Pendulum environment for reinforcement learning (#1388) and update Mountain Car environment (#1394).

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.

This release features the work of our 8 GSoC 2014 students [student; mentors]:

• OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
• Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
• Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
• Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
• Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
• Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
• Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
• Variational Learning for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]

It also contains several cleanups and bugfixes:

Features

• New Shogun project description [Heiko Strathmann]
• ID3 algorithm for decision tree learning [Parijat Mazumdar]
• New modes for PCA matrix factorizations: SVD & EVD, in-place or reallocating [Parijat Mazumdar]
• Add Neural Networks with linear, logistic and softmax neurons [Khaled Nasr]
• Add kernel multiclass strategy examples in multiclass notebook [Saurabh Mahindre]
• Add decision trees notebook containing examples for ID3 algorithm [Parijat Mazumdar]
• Add sudoku recognizer ipython notebook [Alejandro Hernandez]
• Add in-place subsets on features, labels, and custom kernels [Heiko Strathmann]
• Add Principal Component Analysis notebook [Abhijeet Kislay]
• Add Multiple Kernel Learning notebook [Saurabh Mahindre]
• Add Multi-Label classes to enable Multi-Label classification [Thoralf Klein]
• Add rectified linear neurons, dropout and max-norm regularization to neural networks [Khaled Nasr]
• Add C4.5 algorithm for multiclass classification using decision trees [Parijat Mazumdar]
• Add support for arbitrary acyclic graph-structured neural networks [Khaled Nasr]
• Add CART algorithm for classification and regression using decision trees [Parijat Mazumdar]
• Add CHAID algorithm for multiclass classification and regression using decision trees [Parijat Mazumdar]
• Add Convolutional Neural Networks [Khaled Nasr]
• Add Random Forests algorithm for ensemble learning using CART [Parijat Mazumdar]
• Add Restricted Botlzmann Machines [Khaled Nasr]
• Add Stochastic Gradient Boosting algorithm for ensemble learning [Parijat Mazumdar]
• Add Deep contractive and denoising autoencoders [Khaled Nasr]
• Add Deep belief networks [Khaled Nasr]

Bugfixes

• Fix reference counting bugs in CList when reference counting is on [Heiko Strathmann, Thoralf Klein, lambday]
• Fix memory problem in PCA::apply_to_feature_matrix [Parijat Mazumdar]
• Fix crash in LeastAngleRegression for the case D greater than N [Parijat Mazumdar]
• Fix memory violations in bundle method solvers [Thoralf Klein]
• Fix fail in library_mldatahdf5.cpp example when http://mldata.org is not working properly [Parijat Mazumdar]
• Fix memory leaks in Vowpal Wabbit, LibSVMFile and KernelPCA [Thoralf Klein]
• Fix memory and control flow issues discovered by Coverity [Thoralf Klein]
• Fix R modular interface SWIG typemap (Requires SWIG >= 2.0.5) [Matt Huska]

Cleanup and API Changes

• PCA now depends on Eigen3 instead of LAPACK [Parijat Mazumdar]
• Removing redundant and fixing implicit imports [Thoralf Klein]
• Hide many methods from SWIG, reducing compile memory by 500MiB [Heiko Strathmann, Fernando Iglesias, Thoralf Klein]

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.

Version 1.9:

• Replaced drwnInPaint class with drwnImageInPainter class and added inPaint application
• Added function to read CIFAR-10 and CIFAR-100 style datasets (see http://www.cs.utoronto.ca/~kriz/cifar.html)
• Added drwnMaskedPatchMatch, drwnBasicPatchMatch, drwnSelfPatchMatch and basicPatchMatch application
• drwnPatchMatchGraph now allows multiple matches to the same image
• Upgraded wxWidgets to 3.0.2 (problems on Mac OS X)
• Switched Mac OS X compilation to libc++ instead of libstdc++
• Added Python scripts for running experiments and regression tests
• Refactored drwnGrabCutInstance class to support both GMM and colour histogram model
• Added cacheSortIndex to drwnDecisionTree for trading-off speed versus memory usage
• Added mexLoadPatchMatchGraph for loading drwnPatchMatchGraph objects into Matlab
• Improved documentation, other bug fixes and performance improvements

About: The scikit-learn project is a machine learning library in Python.

Update for 0.18 .1

About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

2016-06-09 Version 4.7

Development and release branches available at https://github.com/gpstuff-dev/gpstuff

New features

• Simple Bayesian Optimization demo

Improvements

• Improved use of PSIS
• More options added to gp_monotonic
• Monotonicity now works for additive covariance functions with selected variables
• Possibility to use gpcf_squared.m-covariance function with derivative observations/monotonicity
• Default behaviour made more robust by changing default jitter from 1e-9 to 1e-6
• LA-LOO uses the cavity method as the default (see Vehtari et al (2016). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. JMLR, accpeted for publication)
• Selected variables -option works now better with monotonicity

Bugfixes

• small error in derivative observation computation fixed
• several minor bug fixes

About: A Tool for Embedding Strings in Vector Spaces

Support for explicit selection of granularity added. Several minor bug fixes. We have reached 1.0

About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.

About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models.

Logdet-estimation functionality for grid-based approximate covariances

• Lanczos subspace estimation

• Chebyshef polynomial expansion

More generic infEP functionality

• dense computations and sparse approximations using the same code

• covering KL inference as a special cas of EP

New infKL function contributed by Emtiyaz Khan and Wu Lin

• Conjugate-Computation Variational Inference algorithm

• much more scalable than previous versions

Time-series covariance functions on the positive real line

• covW (i-times integrated) Wiener process covariance

• covOU (i-times integrated) Ornstein-Uhlenbeck process covariance (contributed by Juan Pablo Carbajal)

• covULL underdamped linear Langevin process covariance (contributed by Robert MacKay)

• covFBM Fractional Brownian motion covariance

New covariance functions

• covWarp implements k(w(x),w(z)) where w is a "warping" function

• covMatern has been extended to also accept non-integer distance parameters

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html