机器学习推荐论文和书籍
转自http://blog.csdn.net/chl033/article/details/4822922
好好地学习吧。。
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(ThuOct3021:00:392008),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(HiddenMarkovModels):
ATutorialonHiddenMarkovModelsandSelectedApplicationsin
SpeechRecognition.pdf
ME(MaximumEntropy):
ME_to_NLP.pdf
MEMM(MaximumEntropyMarkovModels):
memm.pdf
CRF(ConditionalRandomFields):
AnIntroductiontoConditionalRandomFieldsforRelationalLearning.pdf
ConditionalRandomFields:ProbabilisticModelsforSegmentingand
LabelingSequenceData.pdf
SVM(supportvectormachine):
*张学工<<统计学习理论>>
LSA(orLSI)(LatentSemanticAnalysis):
Latentsemanticanalysis.pdf
pLSA(orpLSI)(ProbablisticLatentSemanticAnalysis):
ProbabilisticLatentSemanticAnalysis.pdf
LDA(LatentDirichletAllocation):
LatentDirichletAllocaton.pdf(用variationaltheory+EM算法解模型)
Parameterestimationfortextanalysis.pdf(usingGibbsSampling解模)
NeuralNetworksi(includingHopfieldModel&self-organizingmaps&
Stochasticnetworks&BoltzmannMachineetc.):
NeuralNetworks-ASystematicIntroduction
DiffusionNetworks:
DiffusionNetworks,ProductsofExperts,andFactorAnalysis.pdf
Markovrandomfields:
GeneralizedLinearModel(includinglogisticregressionetc.):
AnintroductiontoGeneralizedLinearModels2nd
ChineseRestrauntModel(DirichletProcesses):
DirichletProcesses,ChineseRestaurantProcessesandallthat.pdf
EstimatingaDirichletDistribution.pdf
=================================================================
Someimportantalgorithms:
EM(ExpectationMaximization):
ExpectationMaximizationandPosteriorConstraints.pdf
MaximumLikelihoodfromIncompleteDataviatheEMAlgorithm.pdf
MCMC(MarkovChainMonteCarlo)&GibbsSampling:
MarkovChainMonteCarloandGibbsSampling.pdf
ExplainingtheGibbsSampler.pdf
AnintroductiontoMCMCforMachineLearning.pdf
PageRank:
矩阵分解算法:
SVD,QR分解,Shur分解,LU分解,谱分解
Boosting(includingAdaboost):
*adaboost_talk.pdf
SpectralClustering:
Tutorialonspectralclustering.pdf
Energy-BasedLearning:
AtutorialonEnergy-basedlearning.pdf
BeliefPropagation:
UnderstandingBeliefPropagationanditsGeneralizations.pdf
bp.pdf
Constructionfreeenergyapproximationandgeneralizedbelief
propagationalgorithms.pdf
LoopyBeliefPropagationforApproximateInferenceAnEmpiricalStudy.pdf
LoopyBeliefPropagation.pdf
AP(affinityPropagation):
L-BFGS:
<<最优化理论与算法2nd>>chapter10
OnthelimitedmemoryBFGSmethodforlargescaleoptimization.pdf
IIS:
IIS.pdf
=================================================================
理论部分：
概率图(probabilisticnetworks):
AnintroductiontoVariationalMethodsforGraphicalModels.pdf
ProbabilisticNetworks
FactorGraphsandtheSum-ProductAlgorithm.pdf
ConstructingFreeEnergyApproximationsandGeneralizedBelief
PropagationAlgorithms.pdf
*GraphicalModels,exponentialfamilies,andvariationalinference.pdf
VariationalTheory(变分理论，我们只用概率图上的变分):
Tutorialonvarationalapproximationmethods.pdf
AvariationalBayesianframeworkforgraphicalmodels.pdf
variationaltutorial.pdf
InformationTheory:
ElementsofInformationTheory2nd.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>>
MatrixTheory:
矩阵分析与应用.pdf
模式识别：
<<模式识别2nd>>边肇祺
*PatternRecognitionandMachineLearning.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-supervisedlearning:
<>MITPress
semi-supervisedlearningbasedonGraph.pdf
Co-training:
Self-training: