大牛们的blog （人工智能与机器学习）_关于人工智能的blog

大牛们的blog （人工智能与机器学习）


国外人工智能界牛人主页

以前转过一个计算机视觉领域内的牛人简介，现在转一个更宽范围内的牛人简介：

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。）



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


 


 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/