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):
*張學(xué)工<<統(tǒng)計(jì)學(xué)習(xí)理論>>
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:
矩陣分解算法:
SVD, QR分解, Shur分解, LU分解, 譜分解
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:
<<最優(yōu)化理論與算法 2nd>> chapter 10
On the limited memory BFGS method for large scale optimization.pdf
IIS:
IIS.pdf
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理論部分：
概率圖(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(變分理論，我們只用概率圖上的變分):
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
測(cè)度論：
測(cè)度論(Halmos).pdf
測(cè)度論講義(嚴(yán)加安).pdf
概率論:
......
<<概率與測(cè)度論>>
隨機(jī)過(guò)程:
應(yīng)用隨機(jī)過(guò)程 林元烈 2002.pdf
<<隨機(jī)數(shù)學(xué)引論>>
Matrix Theory:
矩陣分析與應(yīng)用.pdf
模式識(shí)別：
<<模式識(shí)別 2nd>> 邊肇祺
*Pattern Recognition and Machine Learning.pdf
最優(yōu)化理論:
<<Convex Optimization>>
<<最優(yōu)化理論與算法>>
泛函分析：
<<泛函分析導(dǎo)論及應(yīng)用>>
Kernel理論：
<<模式分析的核方法>>
統(tǒng)計(jì)學(xué):
......
<<統(tǒng)計(jì)手冊(cè)>>
==========================================================
綜合:
semi-supervised learning:
<<Semi-supervised Learning>> MIT Press
semi-supervised learning based on Graph.pdf
Co-training:
Self-training:
