Interested Topics & Related Researchers
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Kernel Methods 
Alexander J. Smola
Maximum Mean Discrepancy (MMD), HilbertSchmidt Independence Criterion (HSIC)
Bernhard Schölkopf
Kernel PCA
James T Kwok
PreImage, Kernel Learning, Core Vector Machine(CVM)
DitYan Yang
Kernel methods
Ivor Tsang
Core Vector Machine(CVM), Large Scale Machine Learning
Francis Bach
Graphical Model, Kernelbased Learning, vision and signal proces
Jieping Ye
Kernel Learning, Linear Discriminate Analysis, Dimension Deduction 
MultiTask Learning 
Andreas Argyriou
MultiTask Feature Learning
Charles A. Micchelli
MultiTask Feature Learning, MultiTask Kernel Learning
Massimiliano Pontil
MultiTask Feature Learning
Yiming Ying
MultiTask Feature Learning, MultiTask Kernel Learning 
SemiSupervised Learning 
Manifold Regularization, Laplacian Eigenmaps
Manifold Regularization, Laplacian Eigenmaps
Manifold Regularization
Graphbased Semisupervised Learning 
Multiple Instance Learning 
Sally A Goldman
EMDD, DDSVM, Multiple Instance Semi Supervised Learning(MISS) 
Dimensional Deduction 
Gaussian Process Latent Variable Models (GPLVM)
Maximum Variance Unfolding(MVU), Semidefinite Embedding(SDE)
Fei Sha
Large Margin method, Dimension Deduction 
Structure Output 
Jason Weston
Large Scale machine learning method, semisupervised learning, structrure output
Yasemin Altun
HMMSVM, Semisupervised structure learning
Ben Taskar
MaxMargin Markov Networks
Thorston Joachims
svmlight, svmstruct, svmperf

Machine Learning 
Thomas G. Dietterich
Machine Learning and AI foundations with applications to problems in science and engineering
Michael I. Jordan
Graphical Models
John Lafferty
Diffusion Kernels, Graphical Models
Daphne Koller
Logic, Probability
Avrim Blum
machine learning theory, approximation algorithms, online algorithms, and algorithmic game theory
Theoretical Analysis of Statistical Algorithms, Multitask Learning, Graphbased Semisupervised Learning
Bayesian approaches to machine learning
Machine Learning Department in Toronto University
Machine Learning, web text retrieval, contentbased image retrieval
Andrew NG
Deep learning
Qiang Yang
Transfer Learning and Applications 
Statistical Machine Learning & Optimization 
Jerome H Friedman
GLasso, Statistical view of AdaBoost, Greedy Function Approximation
Rob Tibshirani
Lasso
Thevor Hastie
Lasso
Stephen Boyd
Convex Optimization
C.J Lin
Libsvm
Yurii Nesterov
Optimal Method
Arkadii Nemirovski
Stochastic Method
Stephen J. Wright
Numerial Optimization 
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Last modified: Sep. 23th, 2008
