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]:

OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]

LargeScale MultiLabel Classification [Abinash Panda; Thoralf Klein]

Largescale 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, inplace 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 inplace subsets on features, labels, and custom kernels [Heiko Strathmann]

Add Principal Component Analysis notebook [Abhijeet Kislay]

Add Multiple Kernel Learning notebook [Saurabh Mahindre]

Add MultiLabel classes to enable MultiLabel classification [Thoralf Klein]

Add rectified linear neurons, dropout and maxnorm regularization to neural networks [Khaled Nasr]

Add C4.5 algorithm for multiclass classification using decision trees [Parijat Mazumdar]

Add support for arbitrary acyclic graphstructured 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]

 Operating System:
Cygwin,
Linux,
Macosx,
Bsd
 Data Formats:
Plain Ascii,
Svmlight,
Binary,
Fasta,
Fastq,
Hdf
 JMLRMLOSS Publication:
JMLR Page
 Tags:
Bioinformatics,
Large Scale,
String Kernel,
Kernel,
Kernelmachine,
Lda,
Lpm,
Matlab,
Mkl,
Octave,
Python,
R,
Svm,
Sgd,
Icml2010,
Liblinear,
Libsvm,
Multiple Kernel Learning,
Ocas,
Gaussian Processes,
Reg

