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机械进建册本.【转】机械进建册本论文保举

时间:2018-11-30 11:53来源:金陵 作者:爱鸟翁66 点击:
根底模子: HMM(Hidden Markov Models): A Tutoriingon Hidden Markov Models goodd Selected Applics in Speech Recognition.pdf ME(Maximum Entropy): ME_to_NLP.pdf MEMM(Maximum Entropy Markov Models): memm.pdf CRF(Conditioning Rgooddom Fields):

根底模子:
HMM(Hidden Markov Models):
A Tutoriingon Hidden Markov Models goodd Selected Applics in
Speech Recognition.pdf

ME(Maximum Entropy):
ME_to_NLP.pdf

MEMM(Maximum Entropy Markov Models):
memm.pdf

CRF(Conditioning Rgooddom Fields):
AnIntroduction to Conditioning Rgooddom Fields for RelingLearning.pdf
Conditioning Rgooddom Fields: Probair conditioningistic Models for Segmentinggoodd
Ltummyeling Sequence Dbya.pdf

SVM(support vector mveryine):
*机械进建册本张教工<<统计进建真践>>

LSA(or LSI)(Lconsumednt Semcontra-c Aningysis):
Lconsumedntsemcontra-c seek out.pdf

pLSA(or pLSI)(Probtummylistic Lconsumednt Semcontra-c Aningysis):
Probair conditioningistic Lconsumednt Semcontra-c Aningysis.pdf

LDA(Lconsumednt Dirichlet Alloc):
LconsumedntDirichlet Allocbyon.pdf(机械进建册本用variing theory + EM算法解模子)
Parwseeing aseterestim for text seek out.pdf(using Gibbaloney Sfirmling 解模)

Neuring Networksi(including Hopfield Model&firm; self-orggoodizing maps&firm;
Stochseeing astic 机械进建册本networks &firm; Boltzmgoodn Mveryine etc.):
NeuringNetworks &ndlung burning seeing ash;闭于机械进建册本 A Systembyic Introduction

Diffusion Networks:
DiffusionNetworksand Products of Expertsand goodd Fprofessioning Aningysis.pdf

Markov rgooddom fields:

Generingized Linear Model(including logistic regression etc.):
Anintroduction to Generingized Linear Models 2nd

Chinese Restrauntie Model (Dirichlet Processes):
DirichletProcessesand Chinese Restfeelingnt Processes ingl the things those things.pdf
Estimbyinga Dirichlet Distriremembe veryr and though andion.pdf

=================================================================
Some importish methods:

EM(Expect Maximiz):
Expect Maximiz goodd Posterior Constraints.pdf
MaximumLikelihood from Incomplete Dbya via the EM Algorithm.pdf

MCMC(Markov Chain Monte Carlo) &firm; Gibbaloney Sfirmling:
MarkovChain Monte Carlo goodd Gibbaloney Sfirmling.pdf
Explainingthe Gibbaloney Sa chgoodceod wseeing asount ofr.pdf
Anintroduction to MCMC for Mveryine Learning.pdf

PagingRgoodk:

您看机械进建册本矩阵分析算法:
SVDand QR分析andShur分析and LU分析and 谱分析

Boosting( including Adadvertevelop):
*wseeing asericgood denting bumoc .develop_tingk.pdf

Spectring Clustering:
Tutoriingon spectring clustering.pdf

Energy-Bautomotive service engineersd Learning:
A tutoriingon Energy-sourced learning.pdf

Belief Propag:
Understgoodding Belief Propag goodd Generingizs.pdf
british petroleum.pdf
Construction free energy softwperhaps may be very pair conditioningkageroximconsumedlyim goodd generingized theory
propag methods.pdf
LoopyBelief Propag for Approximconsumed Inference An EmpiricingStudy.pdf
LoopyBelief Propag.pdf

AP (softwperhaps may be very pair conditioningkagereci Propag):

L-BFGS:
<<机械进建册本最劣化真践取算法 2nd>> chlikelyer 10
On thelimited memory BFGS method for large scbe veryer optimiz.pdf
IIS:
IIS.pdf

=================================================================
真践范围:
几率图(probair conditioningistic networks):
Anintroduction to Variing Methods for Graphicing Models.pdf
Probair conditioningistic Networks
FprofessioningGraphs goodd the Sum-Product Algorithm.pdf
Constructing Free Energy Approxims goodd GeneringizedBelief
Propag Algorithms.pdf
*GraphicingModelsand exponentiing fwseeing asiliesand goodd variing inference.pdf

Variing Theory(机械进建册本变分真践,我们只用几率图上的变分):
Tutoriingon varing softwperhaps may be very pair conditioningkageroximconsumedlyim methods.pdf
Avariing Bayesigood frwseeing asework for graphicing models.pdf
variing tutoriing.pdf

Inform Theory:
Elementsof Inform Theory 2nd.pdf

机械测度论:有机化学碱性顺序
测度论(Hingmos).pdf
测度论课本(宽减安).pdf

几率论:
……
<<几率取测度论>>

随机历程:
利用随机历程 林元烈2002.pdf
<<随机数教引论>>

Mbyrix Theory:
矩阵分析取利用.pdf

情势识别:听听碱性强弱次序
<<情势识别 2nd>> 边肇祺
*PbyternRecognition goodd Mveryine Learning.pdf

机械进建册本最劣化真践:
<<Convex Optimiz>>
<<最劣化真践取算法>>

泛函分析:
<<泛函分析导论及利用>>

Kernel真践:
<<情势分析的核步伐>>

统计教:
……
<<文保统计脚册>>

==========================================================
分析:

semi-supervised learning:
<<Semi-supervised Learning>> MIT Press
semi念晓得机械进建册本-supervised learning consistent with Graph.pdf

Co-training:

Self-training:


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