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 ================================================================= 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: <<Convex Optimization>> <<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: <<Semi-supervised Learning>> MIT Press semi-supervised learning based on Graph.pdf Co-training: Self-training:
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