國外人工智慧界牛人首頁

國外人工智慧界牛人首頁 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演算法原始碼及其相關論文)

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

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

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首頁)

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

http://web.engr.oregonstate.edu/~tgd/    （國際著名的人工智慧專家 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，裡面有與教材匹配的slide。）

 

Kernel Methods

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

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/

模式識別和神經網路工具箱

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

機器學習開原始碼

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

統計學開原始碼

http://www.wessa.net/

matlab各種工具箱連結

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

統計學學習經典線上教材

http://www.statistics4u.info/

機器學習開源原始碼

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

 

 

From:http://tzczsq.blog.163.com/blog/static/22603058201102602452399/

http://www.cnblogs.com/skyseraph/