国外AI界牛人主页 及资源链接
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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/
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