machine learning中的重要延伸模型

时间:2021-06-03 06:23:13

机器学习推荐论文和书籍(转载)

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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。:)

======================================
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: