We integrated the work of the following people:
Alex J. Smola - the pr_loqo optimizer
Antoine Bordes - LaRank
Thorsten Joachims - SVMLight
Chih-Chung Chang and and Chih-Jen Lin - LibSVM
Xiang-Rui Wang and Chih-Jen Lin - LIBLINEAR
Thomas Serafini, Luca Zanni, Gaetano Zanghirati - the Gradient Projection Decomposition Technique (GPDT) - SVM
Vikas Sindhwani - SVM-lin: Fast SVM Solvers for Supervised and Semi-supervised Learning
Vojtech Franc - Generalized Nearest Point Problem Solver based L2 (slacks) SVM - Optimized Cutting Plane Support Vector Machines (Ocas)
Jean-Philippe Vert and Hiroto Saigo - Local Alignment Kernel
Leon Bottou - Stochastic Gradient Descent (SGD) SVM
Marius Kloft - 2-norm and q-norm MKL - SMO based true Multi-Class SVM
Alexander Zien - Newton based q-norm MKL - POIM code for WD kernels
Christian Gehl - Distance Metrics
Christian Widmer - Dual and Multitask Learning - Serialization support
Jonas Behr - Structured Learning
Elpmis Lee - Translation of the documentation to Chinese
Baozeng Ding - Support for modular java, c#, ruby, lua interfaces
Shashwat Lal Das - Streaming / Online Feature Framework for SimpleFeatures, SparseFeatures, StringFeatures, SGD-QN, Online SGD, Online Liblinear, Online Vowpal Vabit
Heiko Strathmann - Model selection/Cross-validation for arbitrary Machines - Statistics module - Subset support in features - Various bugfixes and structural improvements - Serialization improvements and fixes/ Migration framework - Machine Locking for precomputed kernel matrices - Statistical hypothesis testing framework / Kernel Two-Sample/Independence tests
Alesis Novik - Gaussian Mixture Models
Evgeniy Andreev: - FibonacciHeap - Python 3 support - CoverTree - HashSet
Justin Patera - Ruby examples
Daniel Korn - C# examples
Fernando José Iglesias Garcia - Generic multiclass OvO training - Quadratic Discriminant Analysis
J. Liu, S. Ji and J. Ye - SLEP: A Sparse Learning Package C and ported code
J. Zhou, J. Chen and J. Ye - MALSAR: Multi-tAsk Learning via StructurAL Regularization ported code