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]