国外人工智能界牛人主页

转载自：http://caiqi1123.blog.163.com/blog/static/5736178120080213836366/

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。）

*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) 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)
*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