1 Individual Contributions {#contributions}
2 ========================
5 Let the numbers speak: number of commits per developer, cut-off at 50 (last updated: 9. Nov 2014)
7 $ git log --format=
'%aN' | sort | uniq -c | sort -nr
13 560 Sebastian Henschel
37 ## Google Summer of Code Projects
38 We greatly appreciate the support by Google and the hard work of our students and mentors!
41 * OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
42 * Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
43 * Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
44 * Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
45 * Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
46 * Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
47 * Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
48 * Variational Learning
for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]
51 * Gaussian Processes
for binary classification [Roman Votjakov]
52 * Sampling log-determinants
for large sparse matrices [Soumyajit De]
53 * Metric Learning via LMNN [Fernando Iglesias]
54 * Independent Component Analysis (ICA) [Kevin Hughes]
55 * Hashing Feature Framework [Evangelos Anagnostopoulos]
56 * Structured Output Learning [Hu Shell]
57 * A web-demo framework [Liu Zhengyang]
60 * Kernel Hypothesis Testing [Heiko Strathmann]
61 * Latent SVM [Viktor Gal]
62 * Multitask Learning [Sergey Listsyn]
63 * Bundle Methods [Michal Uricar]
64 * Multiclass methods [Chiyuan Zhang]
65 * Gaussian Process regression [Jacob Walker]
66 * Structured Output Framework [Fernando Iglesias]
69 * Support
for new languages [Baozeng Ding]
70 * Dimensionality reduction algorithms [Sergey Lisitsyn]
71 * Streaming / Online Feature Framework [Shashwat Lal Das]
72 * Model selection framework [Heiko Strathmann]
73 * Gaussian Mixture Models [Alesis Novik]
75 ## Individual contributions
77 * the pr_loqo optimizer
85 Chih-Chung Chang and and Chih-Jen Lin
88 Xiang-Rui Wang and Chih-Jen Lin
91 Thomas Serafini, Luca Zanni, Gaetano Zanghirati
92 * the Gradient Projection Decomposition Technique (GPDT) - SVM
95 * SVM-lin: Fast SVM Solvers
for Supervised and Semi-supervised Learning
98 * Generalized Nearest Point Problem Solver based L2 (slacks) SVM
99 * Optimized Cutting Plane Support Vector Machines (Ocas)
101 Jean-Philippe Vert and Hiroto Saigo
102 * Local Alignment Kernel
105 * Stochastic Gradient Descent (SGD) SVM
109 * SMO based true Multi-Class SVM
112 * Newton based q-
norm MKL
113 * POIM code for WD kernels
119 * Dual and Multitask Learning
120 * Serialization support
123 * Structured Learning
126 * Translation of the documentation to Chinese
129 * Support for modular java, c
#, ruby, lua interfaces
132 * Streaming / Online Feature Framework
for SimpleFeatures, SparseFeatures, StringFeatures, SGD-QN, Online SGD, Online Liblinear, Online Vowpal Vabit
135 * Model selection/Cross-validation
for arbitrary Machines
137 * Subset support in features
138 * Various bugfixes and structural improvements
139 * Serialization improvements and fixes/ Migration framework
140 * Machine Locking
for precomputed kernel matrices
141 * Statistical hypothesis testing framework / Kernel Two-Sample/Independence tests
144 * Gaussian Mixture Models
158 Fernando José Iglesias Garcia
159 * Generic multiclass OvO training
160 * Quadratic Discriminant Analysis
161 * Metric Learning via LMNN
163 J. Liu, S. Ji and J. Ye
164 * SLEP: A Sparse Learning Package C and ported code
166 J. Zhou, J. Chen and J. Ye
167 * MALSAR: Multi-tAsk Learning via StructurAL Regularization ported code
170 We also acknowledge support from Alexander Binder, Alexander Zien, Andre Noll, Cheng Soon Ong, Christian Gehl, Christian Widmer, Christoph Lampert, Fabio De Bona, Jonas Behr, Konrad Rieck, Mikio Braun, Torsten Werner, Vojtech Franc, Yaroslav Halchenko
double norm(double *v, double p, int n)
void Thomas(double *zMax, double *z0, double *Av, int nn)