转载自：http://caiqi1123.blog.163.com/blog/static/5736178120080213836366/
(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）
(lle算法源代码及其相关论文)
（data clustering）
(里面有好多资源)
(manifold learning)
(manifold learning demo in matlab)
(manifold learning in matlab)
(semi supervised learning with manifold method by Belkin)
(isomap主页)
MIT TENENBAUM J B主页
（国际著名的人工智能专家 Thomas G. Dietterich）
（MIchael I.Jordan）
(Andrew W. Moore's homepage)
（加拿大多伦多大学机器学习小组）
（Tom Mitchell，里面有与教材匹配的slide。）
| Kernel Methods | |
|
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 | |
| 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 | |
| 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 |
半监督流形学习（流形正则化）
模式识别和神经网络工具箱
机器学习开源代码
统计学开源代码
matlab各种工具箱链接
统计学学习经典在线教材
机器学习开源源代码