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！！！

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