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
=================================================================
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:
原文地址：http://www.cnblogs.com/focus-ml/p/3755315.html
