感覺 好博客要收集，還是貼在自己空間里難忘！！！

原文鏈接：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.?)（國內已經有相應的中文版）

http://www.cs.toronto.edu/~roweis/lle/publications.html??? (lle算法源代碼及其相關論文)

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

http://www.cs.toronto.edu/~roweis/???? (里面有好多資源)

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主頁)

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

http://web.engr.oregonstate.edu/~tgd/??? （國際著名的人工智能專家 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/?（加拿大多倫多大學機器學習小組）

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

?

一些牛人索引：

Kernel Methods：核方法到底有何好處呢？？？？

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：半監督學習...

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/

半監督流形學習（流形正則化）

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

模式識別和神經網絡工具箱

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

機器學習開源代碼

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

統計學開源代碼

http://www.wessa.net/

matlab各種工具箱鏈接

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

統計學學習經典在線教材

http://www.statistics4u.info/

機器學習開源源代碼

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

總結

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