Interested Topics & Related Researchers

Kernel Methods Multi Task Learning Semi Supervised Multiple Instance Learning
Dinmensional Deduction Structure Output Machine Learning Statistical & Optimization

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Kernel Methods

Alexander J. Smola

Maximum Mean Discrepancy (MMD), Hilbert-Schmidt Independence Criterion (HSIC)

Bernhard Schölkopf

Kernel PCA

James T Kwok

Pre-Image, Kernel Learning, Core Vector Machine(CVM)

Dit-Yan Yang

Kernel methods

Ivor Tsang

Core Vector Machine(CVM), Large Scale Machine Learning

Francis Bach

Graphical Model, Kernel-based Learning, vision and signal proces

Jieping Ye

Kernel Learning, Linear Discriminate Analysis, Dimension Deduction

Multi-Task Learning

Andreas Argyriou

Multi-Task Feature Learning

Charles A. Micchelli

Multi-Task Feature Learning, Multi-Task Kernel Learning

Massimiliano Pontil

Multi-Task Feature Learning

Yiming Ying

Multi-Task Feature Learning, Multi-Task Kernel Learning

Semi-Supervised Learning

Partha Niyogi

Manifold Regularization, Laplacian Eigenmaps

Mikhail Belkin

Manifold Regularization, Laplacian Eigenmaps

Vikas Sindhwani

Manifold Regularization

Xiaojin Zhu

Graph-based Semi-supervised Learning

Multiple Instance Learning

Sally A Goldman

EM-DD, DD-SVM, Multiple Instance Semi Supervised Learning(MISS)

Dimensional Deduction

Neil Lawrence

Gaussian Process Latent Variable Models (GPLVM)

Lawrence K. Saul

Maximum Variance Unfolding(MVU), Semidefinite Embedding(SDE)

Fei Sha

Large Margin method, Dimension Deduction

Structure Output

Jason Weston

Large Scale machine learning method, semi-supervised learning, structrure output

Yasemin Altun

HMM-SVM, Semi-supervised structure learning

Ben Taskar

Max-Margin Markov Networks

Thorston Joachims

svm-light, svm-struct, svm-perf

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, on-line algorithms, and algorithmic game theory

Zhang Tong

Theoretical Analysis of Statistical Algorithms, Multi-task Learning, Graph-based Semi-supervised Learning

Zoubin Ghahramani

Bayesian approaches to machine learning

Machine Learning @ Toronto

Machine Learning Department in Toronto University

Rong Jin

Machine Learning, web text retrieval, content-based 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


Thevor Hastie


Stephen Boyd

Convex Optimization

C.J Lin


Yurii Nesterov

Optimal Method

Arkadii Nemirovski

Stochastic Method

Stephen J. Wright

Numerial Optimization


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Last modified: Sep. 23th, 2008