发信人: zibuyu (得之我幸), 信区: NLP 
标 题: 机器学习推荐论文和书籍 
发信站: 水木社区 (Thu Oct 30 21:00:39 2008), 站内 
我们组内某小神童师弟通读论文，拟了一个机器学习的推荐论文和书籍列表。 
经授权发布在这儿，希望对大家有用。:) 
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基本模型: 
HMM(Hidden Markov Models): 
A Tutorial on Hidden Markov Models and Selected Applications in 
Speech Recognition.pdf 
ME(Maximum Entropy): 
ME_to_NLP.pdf 
MEMM(Maximum Entropy Markov Models): 
memm.pdf 
CRF(Conditional Random Fields): 
An Introduction to Conditional Random Fields for Relational Learning.pdf 
Conditional Random Fields: Probabilistic Models for Segmenting and 
Labeling Sequence Data.pdf 
SVM(support vector machine): 
*张学工<<统计学习理论>> 
LSA(or LSI)(Latent Semantic Analysis): 
Latent semantic analysis.pdf 
pLSA(or pLSI)(Probablistic Latent Semantic Analysis): 
Probabilistic Latent Semantic Analysis.pdf 
LDA(Latent Dirichlet Allocation): 
Latent Dirichlet Allocaton.pdf(用variational theory + EM算法解模型) 
Parameter estimation for text analysis.pdf(using Gibbs Sampling 解模) 
Neural Networksi(including Hopfield Model& self-organizing maps & 
Stochastic networks & Boltzmann Machine etc.): 
Neural Networks - A Systematic Introduction 
Diffusion Networks: 
Diffusion Networks, Products of Experts, and Factor Analysis.pdf 
Markov random fields: 
Generalized Linear Model(including logistic regression etc.): 
An introduction to Generalized Linear Models 2nd 
Chinese Restraunt Model (Dirichlet Processes): 
Dirichlet Processes, Chinese Restaurant Processes and all that.pdf 
Estimating a Dirichlet Distribution.pdf 
================================================================= 
Some important algorithms: 
EM(Expectation Maximization): 
Expectation Maximization and Posterior Constraints.pdf 
Maximum Likelihood from Incomplete Data via the EM Algorithm.pdf 
MCMC(Markov Chain Monte Carlo) & Gibbs Sampling: 
Markov Chain Monte Carlo and Gibbs Sampling.pdf 
Explaining the Gibbs Sampler.pdf 
An introduction to MCMC for Machine Learning.pdf 
PageRank: 
矩阵分解算法: 
SVD, QR分解, Shur分解, LU分解, 谱分解 
Boosting( including Adaboost): 
*adaboost_talk.pdf 
Spectral Clustering: 
Tutorial on spectral clustering.pdf 
Energy-Based Learning: 
A tutorial on Energy-based learning.pdf 
Belief Propagation: 
Understanding Belief Propagation and its Generalizations.pdf 
bp.pdf 
Construction free energy approximation and generalized belief 
propagation algorithms.pdf 
Loopy Belief Propagation for Approximate Inference An Empirical Study.pdf 
Loopy Belief Propagation.pdf 
AP (affinity Propagation): 
L-BFGS: 
<<最优化理论与算法 2nd>> chapter 10 
On the limited memory BFGS method for large scale optimization.pdf 
IIS: 
IIS.pdf 
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理论部分： 
概率图(probabilistic networks): 
An introduction to Variational Methods for Graphical Models.pdf 
Probabilistic Networks 
Factor Graphs and the Sum-Product Algorithm.pdf 
Constructing Free Energy Approximations and Generalized Belief 
Propagation Algorithms.pdf 
*Graphical Models, exponential families, and variational inference.pdf 
Variational Theory(变分理论，我们只用概率图上的变分): 
Tutorial on varational approximation methods.pdf 
A variational Bayesian framework for graphical models.pdf 
variational tutorial.pdf 
Information Theory: 
Elements of Information Theory 2nd.pdf 
测度论： 
测度论(Halmos).pdf 
测度论讲义(严加安).pdf 
概率论: 
...... 
<<概率与测度论>> 
随机过程: 
应用随机过程 林元烈 2002.pdf 
<<随机数学引论>> 
Matrix Theory: 
矩阵分析与应用.pdf 
模式识别： 
<<模式识别 2nd>> 边肇祺 
*Pattern Recognition and Machine Learning.pdf 
最优化理论: 
<> 
<<最优化理论与算法>> 
泛函分析： 
<<泛函分析导论及应用>> 
Kernel理论： 
<<模式分析的核方法>> 
统计学: 
...... 
<<统计手册>> 
========================================================== 
综合: 
semi-supervised learning: 
<> MIT Press 
semi-supervised learning based on Graph.pdf 
Co-training: 
Self-training: