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.

• Fix compilation error with recent gcc

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: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:

1. Support for multithreading in training and prediction with ensemble models. Since both of these are embarassingly parallel, this has induced a significant speedup (3-fold on quad-core).
2. Extensive programming framework for aggregation of base model predictions which allows highly efficient prototyping of new aggregation approaches. Additionally we provide several predefined strategies, including (weighted) majority voting, logistic regression and nonlinear SVMs of your choice -- be sure to check out the esvm-edit tool! The provided framework also allows you to efficiently program your own, novel aggregation schemes.
3. Full code transition to C++11, the latest C++ standard, which enabled various performance improvements. The new release requires moderately recent compilers, such as gcc 4.7.2+ or clang 3.2+.
4. Generic implementations of convenient facilities have been added, such as thread pools, deserialization factories and more.

The API and ABI have undergone significant changes, many of which are due to the transition to C++11.