国外AI界牛人主页 及资源链接

感觉 好博客要收集，还是贴在自己空间里难忘！！！

原文链接：http://blog.csdn.net/hitwengqi/article/details/7907366

http://people.cs.uchicago.edu/~niyogi/

http://www.cs.uchicago.edu/people/

http://pages.cs.wisc.edu/~jerryzhu/

http://www.kyb.tuebingen.mpg.de/~chapelle

http://people.cs.uchicago.edu/~xiaofei/

http://www.cs.uiuc.edu/homes/dengcai2/

http://www.kyb.mpg.de/~bs

http://research.microsoft.com/~denzho/

http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item5    (resources for the
book of the introduction of data mining by Pang-ning Tan et.al. )（国内已经有相应的中文版）

http://www.cs.toronto.edu/~roweis/lle/publications.html    (lle算法源代码及其相关论文)

http://dataclustering.cse.msu.edu/index.html#software（data clustering）

http://www.cs.toronto.edu/~roweis/     (里面有好多资源)

http://www.cse.msu.edu/~lawhiu/  (manifold learning)

http://www.math.umn.edu/~wittman/mani/ (manifold learning demo in matlab)

http://www.iipl.fudan.edu.cn/~zhangjp/literatures/MLF/INDEX.HTM  (manifold
learning in matlab)

http://videolectures.net/mlss05us_belkin_sslmm/   (semi supervised learning with manifold method
by Belkin)

http://isomap.stanford.edu/    (isomap主页)

http://web.mit.edu/cocosci/josh.html  MIT    TENENBAUM J B主页

http://web.engr.oregonstate.edu/~tgd/    （国际著名的人工智能专家 Thomas G. Dietterich）

http://www.cs.berkeley.edu/~jordan/ （MIchael I.Jordan）

http://www.cs.cmu.edu/~awm/  (Andrew W. Moore's  homepage)

http://learning.cs.toronto.edu/ （加拿大多伦多大学机器学习小组）

http://www.cs.cmu.edu/~tom/ （Tom Mitchell，里面有与教材匹配的slide。）

一些牛人索引：

Kernel Methods：核方法到底有何好处呢？？？？

Alexander J. Smola

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

Bernhard Sch?lkopf

Kernel PCA

James T Kwok

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

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：半监督学习...

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)

Dimensionality Reduction

Neil Lawrence

Gaussian Process Latent Variable Models (GPLVM)

Lawrence K. Saul

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

Machine Learning

Michael I. Jordan

Graphical Models

John Lafferty

Diffusion Kernels, Graphical Models

Daphne Koller

Logic, Probability

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

Statitiscal Machine Learning & Optimization

Jerome H Friedman

GLasso, Statistical view of AdaBoost, Greedy Function Approximation

Thevor Hastie

Lasso

Stephen Boyd

Convex Optimization

C.J Lin

Libsvm

http://www.dice.ucl.ac.be/mlg/

半监督流形学习（流形正则化）

http://manifold.cs.uchicago.edu/

模式识别和神经网络工具箱

http://www.ncrg.aston.ac.uk/netlab/index.php

机器学习开源代码

http://mloss.org/software/tags/large-scale-learning/

统计学开源代码

http://www.wessa.net/

matlab各种工具箱链接

http://www.tech.plym.ac.uk/spmc/links/matlab/matlab_toolbox.html

统计学学习经典在线教材

http://www.statistics4u.info/

机器学习开源源代码

http://mloss.org/software/language/matlab/