Shogun 4.0.0 - Kose no Maro

• 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]
• This release 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]