国外AI界牛人主页 及资源链接
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://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???? |
|
Maximum Mean Discrepancy (MMD), Hilbert-Schmidt Independence Criterion (HSIC) Kernel PCA Pre-Image, Kernel Learning, Core Vector Machine(CVM) Kernel Learning, Linear Discriminate Analysis, Dimension Deduction |
|
Multi-Task Learning |
|
Multi-Task Feature Learning Multi-Task Feature Learning, Multi-Task Kernel Learning Multi-Task Feature Learning Multi-Task Feature Learning, Multi-Task Kernel Learning |
|
Semi-supervised Learning:半监督学习... |
|
Partha Niyogi Manifold Regularization, Laplacian Eigenmaps Manifold Regularization, Laplacian Eigenmaps Manifold Regularization Graph-based Semi-supervised Learning |
|
Multiple Instance Learning |
|
EM-DD, DD-SVM, Multiple Instance Semi Supervised Learning(MISS) |
|
Dimensionality Reduction |
|
Neil Lawrence Gaussian Process Latent Variable Models (GPLVM) Maximum Variance Unfolding(MVU), Semidefinite Embedding(SDE) |
|
Machine Learning |
|
Graphical Models Diffusion Kernels, Graphical Models Logic, Probability Theoretical Analysis of Statistical Algorithms, Multi-task Learning, Graph-based Semi-supervised Learning Bayesian approaches to machine learning |
|
Statitiscal Machine Learning & Optimization |
|
GLasso, Statistical view of AdaBoost, Greedy Function Approximation Lasso Convex Optimization 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
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
机器学习开源源代码
http://mloss.org/software/language/matlab/
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