Recommendation of machine learning books and papers

Basic Model:
Hmm (Hidden Markov models ):
A tutorial on Hidden Markov models and selected applications in
Speech recognition.pdf

Me (maximum entropy ):
Me_to_nlp133

Memm (Maximum Entropy Markov models ):
Memmworkflow

CRF (Conditional Random Fields ):
An Introduction to Conditional Random Fields for relational learningregression
Conditional Random Fields: probabilistic models for segmenting and
Labeling sequence data.pdf

SVM (Support Vector Machine ):
* Zhang xuecang <statistical learning theory>

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 (uses the variational theory + EM algorithm to describe the model)
Parameter Estimation for text analysis.pdf (using gibsampling)

Neural networksi (including tmpmodel & Self-Organizing Maps &
Stochastic Networks & Boltzmann Machine etc .):
Neural Networks-a systematic introduction

Diffusion networks:
Diffusion networks, products of experts, and factor analysisworkflow

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 thatses
Estimating a Dirichlet distributionencryption

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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:
Markov Chain Monte Carlo and James sampling.pdf
Explaining the James sampler.pdf
An Introduction to MCMC for machine learning.pdf

PageRank:

Matrix factorization algorithm:
SVD, QR decomposition, Shur decomposition, Lu decomposition, spectral decomposition

Boosting (including Adaboost ):
* Optional st_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 propagationdeletion

AP (affinity propagation ):

L-BFGS:
<Optimization Theory and Algorithm 2nd> Chapter 10
On the limited memory BFGS method for large scale optimizationscaling
IIS:
Iis.pdf

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Theoretical part:
Probability graph (Probabilistic networks ):
An Introduction to Variational Methods for graphical models.pdf
Probabilistic Networks
Factor graphs and the sum-product algorithmhm
Constructing free energy approximations and generalized belief
Propagation algorithms.pdf
* Graphical models, exponential families, and variational inferenceies

Variational theory (variational theory, we only use the variational in the probability graph ):
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

Measurement Theory:
Measurement Theory (halmos).pdf)
Evaluation Comments (Yan jiayan)

Probability Theory:
......
<Probability and measurement theory>

Random Process:
Lin yuanlie 2002.pdf
<Random mathematics introduction>

Matrix Theory:
Matrix Analysis and Application

Pattern recognition:
<Pattern Recognition 2nd> Bian Zhao Qi
* Pattern recognition and machine learningtion

Optimization Theory:
<Convex optimization>
<Optimization Theory and Algorithm>

Functional Analysis:
<Introduction and application of functional analysis>

Kernel theory:
<Core method of mode analysis>

Statistics:
......
<Statistical Manual>

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Overall:

Semi-supervised learning:
<Semi-supervised learning> MIT Press
Semi-Supervised Learning Based on graph.pdf

Co-training:

Self-training:

From: http://hi.baidu.com/kamelzcs/item/ed1b5b2dc0d868de0e37f91e