• Home

  • Documents

  • Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize...
38
CS639: Data Management for Data Science Lecture 19: Unsupervised Learning/Ensemble Learning Theodoros Rekatsinas 1

Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

  • Upload
    others

  • View
    11

  • Download
    0

Embed Size (px)

Citation preview

Page 1: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

CS639:DataManagementfor

DataScienceLecture19:UnsupervisedLearning/EnsembleLearning

TheodorosRekatsinas

1

Page 2: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Today

1. UnsupervisedLearning/Clustering

2. K-Means

3. EnsemblesandGradientBoosting

2

Page 3: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Whatisclustering?

Page 4: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Whatdoweneedforclustering?

Page 5: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Distance(dissimilarity)measures

Page 6: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Clusterevaluation(ahardproblem)

Page 7: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Howmanyclusters?

Page 8: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Clusteringtechniques

Page 9: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Clusteringtechniques

Page 10: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Clusteringtechniques

Page 11: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-means

Page 12: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansalgorithm

Page 13: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansconvergence(stoppingcriterion)

Page 14: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansclusteringexample:step1

Page 15: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansclusteringexample:step2

Page 16: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansclusteringexample:step3

Page 17: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansclusteringexample

Page 18: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansclusteringexample

Page 19: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meansclusteringexample

Page 20: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

WhyuseK-means?

Page 21: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

WeaknessesofK-means

Page 22: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Outliers

Page 23: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Dealingwithoutliers

Page 24: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Sensitivitytoinitialseeds

Page 25: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

K-meanssummary

Page 26: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Tonsofclusteringtechniques

Page 27: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Summary:clustering

Page 28: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Ensemblelearning:FightingtheBias/VarianceTradeoff

28

Page 29: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Ensemblemethods• Combinedifferentmodelstogetherto

• Minimizevariance• Bagging• RandomForests

• Minimizebias• FunctionalGradientDescent• Boosting• EnsembleSelection

Page 30: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Ensemblemethods• Combinedifferentmodelstogetherto

• Minimizevariance• Bagging• RandomForests

• Minimizebias• FunctionalGradientDescent• Boosting• EnsembleSelection

Page 31: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Bagging• Goal:reducevariance

• Idealsetting:manytrainingsetsS’• TrainmodelusingeachS’• Averagepredictions

ES[(h(x|S)- y)2]=ES[(Z-ž)2]+ž2

Variance BiasExpectedError

Z=h(x|S)– yž =ES[Z]

http://statistics.berkeley.edu/sites/default/files/tech-reports/421.pdf“BaggingPredictors” [LeoBreiman,1994]

VariancereduceslinearlyBiasunchanged

sampledindependently

Person+ Age+ Male?+ Height+>+55”+

Alice# 14# 0# 1#

Bob# 10# 1# 1#

Carol# 13# 0# 1#

Dave# 8# 1# 0#

Erin# 11# 0# 0#

Frank# 9# 1# 1#

Gena# 8# 0# 0#

Person+ Age+ Male?+ Height+>+55”+

James# 11# 1# 1#

Jessica# 14# 0# 1#

Alice# 14# 0# 1#

Amy# 12# 0# 1#

Bob# 10# 1# 1#

Xavier# 9# 1# 0#

Cathy# 9# 0# 1#

Carol# 13# 0# 1#

Eugene# 13# 1# 0#

Rafael# 12# 1# 1#

Dave# 8# 1# 0#

Peter# 9# 1# 0#

Henry# 13# 1# 0#

Erin# 11# 0# 0#

Rose# 7# 0# 0#

Iain# 8# 1# 1#

Paulo# 12# 1# 0#

Margaret# 10# 0# 1#

Frank# 9# 1# 1#

Jill# 13# 0# 0#

Leon# 10# 1# 0#

Sarah# 12# 0# 0#

Gena# 8# 0# 0#

Patrick# 5# 1# 1#…+

L(h)#=#E(x,y)~P(x,y)[#f(h(x),y)#]###GeneralizaHon+Error:+

h(x)+y+

Person+ Age+ Male?+ Height+>+55”+

Alice# 14# 0# 1#

Bob# 10# 1# 1#

Carol# 13# 0# 1#

Dave# 8# 1# 0#

Erin# 11# 0# 0#

Frank# 9# 1# 1#

Gena# 8# 0# 0#

Person+ Age+ Male?+ Height+>+55”+

James# 11# 1# 1#

Jessica# 14# 0# 1#

Alice# 14# 0# 1#

Amy# 12# 0# 1#

Bob# 10# 1# 1#

Xavier# 9# 1# 0#

Cathy# 9# 0# 1#

Carol# 13# 0# 1#

Eugene# 13# 1# 0#

Rafael# 12# 1# 1#

Dave# 8# 1# 0#

Peter# 9# 1# 0#

Henry# 13# 1# 0#

Erin# 11# 0# 0#

Rose# 7# 0# 0#

Iain# 8# 1# 1#

Paulo# 12# 1# 0#

Margaret# 10# 0# 1#

Frank# 9# 1# 1#

Jill# 13# 0# 0#

Leon# 10# 1# 0#

Sarah# 12# 0# 0#

Gena# 8# 0# 0#

Patrick# 5# 1# 1#…+

L(h)#=#E(x,y)~P(x,y)[#f(h(x),y)#]###GeneralizaHon+Error:+

h(x)+y+

S’P(x,y)

Page 32: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Bagging

32

• Goal:reducevariance

• Inpractice:resampleS’withreplacement• TrainmodelusingeachS’• Averagepredictions

ES[(h(x|S)- y)2]=ES[(Z-ž)2]+ž2

Variance BiasExpectedError

Z=h(x|S)– yž =ES[Z]

fromS

http://statistics.berkeley.edu/sites/default/files/tech-reports/421.pdf“BaggingPredictors” [LeoBreiman,1994]

Variancereducessub-linearly(BecauseS’arecorrelated)Biasoftenincreasesslightly

Person+ Age+ Male?+ Height+>+55”+

Alice# 14# 0# 1#

Bob# 10# 1# 1#

Carol# 13# 0# 1#

Dave# 8# 1# 0#

Erin# 11# 0# 0#

Frank# 9# 1# 1#

Gena# 8# 0# 0#

Person+ Age+ Male?+ Height+>+55”+

James# 11# 1# 1#

Jessica# 14# 0# 1#

Alice# 14# 0# 1#

Amy# 12# 0# 1#

Bob# 10# 1# 1#

Xavier# 9# 1# 0#

Cathy# 9# 0# 1#

Carol# 13# 0# 1#

Eugene# 13# 1# 0#

Rafael# 12# 1# 1#

Dave# 8# 1# 0#

Peter# 9# 1# 0#

Henry# 13# 1# 0#

Erin# 11# 0# 0#

Rose# 7# 0# 0#

Iain# 8# 1# 1#

Paulo# 12# 1# 0#

Margaret# 10# 0# 1#

Frank# 9# 1# 1#

Jill# 13# 0# 0#

Leon# 10# 1# 0#

Sarah# 12# 0# 0#

Gena# 8# 0# 0#

Patrick# 5# 1# 1#…+

L(h)#=#E(x,y)~P(x,y)[#f(h(x),y)#]###GeneralizaHon+Error:+

h(x)+y+

Person+ Age+ Male?+ Height+>+55”+

Alice# 14# 0# 1#

Bob# 10# 1# 1#

Carol# 13# 0# 1#

Dave# 8# 1# 0#

Erin# 11# 0# 0#

Frank# 9# 1# 1#

Gena# 8# 0# 0#

Person+ Age+ Male?+ Height+>+55”+

James# 11# 1# 1#

Jessica# 14# 0# 1#

Alice# 14# 0# 1#

Amy# 12# 0# 1#

Bob# 10# 1# 1#

Xavier# 9# 1# 0#

Cathy# 9# 0# 1#

Carol# 13# 0# 1#

Eugene# 13# 1# 0#

Rafael# 12# 1# 1#

Dave# 8# 1# 0#

Peter# 9# 1# 0#

Henry# 13# 1# 0#

Erin# 11# 0# 0#

Rose# 7# 0# 0#

Iain# 8# 1# 1#

Paulo# 12# 1# 0#

Margaret# 10# 0# 1#

Frank# 9# 1# 1#

Jill# 13# 0# 0#

Leon# 10# 1# 0#

Sarah# 12# 0# 0#

Gena# 8# 0# 0#

Patrick# 5# 1# 1#…+

L(h)#=#E(x,y)~P(x,y)[#f(h(x),y)#]###GeneralizaHon+Error:+

h(x)+y+

S’S

Bagging=BootstrapAggregation

Page 33: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

RandomForests

33

• Goal: reducevariance• Baggingcanonlydosomuch• Resamplingtrainingdataasymptotes

• RandomForests:sampledata&features!• SampleS’• TrainDT

• Ateachnode,samplefeatures(sqrt)• Averagepredictions

“RandomForests– RandomFeatures”[LeoBreiman,1997]http://oz.berkeley.edu/~breiman/random-forests.pdf

Furtherde-correlatestrees

Page 34: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Ensemblemethods• Combinedifferentmodelstogetherto

• Minimizevariance• Bagging• RandomForests

• Minimizebias• FunctionalGradientDescent• Boosting• EnsembleSelection

Page 35: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

GradientBoosting

35

h(x)=h1(x)

S’={(x,y)}

h1(x)

S’={(x,y-h1(x))}

h2(x)

S’={(x,y-h1(x)- …- hn-1(x))}

hn(x)

…

+h2(x) +…+hn(x)

Page 36: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Boosting(AdaBoost)

36

h(x)=a1h1(x)

S’={(x,y,u1)}

h1(x)

S’={(x,y,u2)}

h2(x)

S’={(x,y,u3))}

hn(x)

…

+a2h2(x)+…+a3hn(x)

https://www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf

u– weightingondatapointsa – weightoflinearcombination Stopwhenvalidation

performanceplateaus(willdiscusslater)

Page 37: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

EnsembleSelection

37

“EnsembleSelectionfromLibrariesofModels”Caruana,Niculescu-Mizil,Crew&Ksikes,ICML2004

Person+ Age+ Male?+ Height+>+55”+

Alice# 14# 0# 1#

Bob# 10# 1# 1#

Carol# 13# 0# 1#

Dave# 8# 1# 0#

Erin# 11# 0# 0#

Frank# 9# 1# 1#

Gena# 8# 0# 0#

Person+ Age+ Male?+ Height+>+55”+

James# 11# 1# 1#

Jessica# 14# 0# 1#

Alice# 14# 0# 1#

Amy# 12# 0# 1#

Bob# 10# 1# 1#

Xavier# 9# 1# 0#

Cathy# 9# 0# 1#

Carol# 13# 0# 1#

Eugene# 13# 1# 0#

Rafael# 12# 1# 1#

Dave# 8# 1# 0#

Peter# 9# 1# 0#

Henry# 13# 1# 0#

Erin# 11# 0# 0#

Rose# 7# 0# 0#

Iain# 8# 1# 1#

Paulo# 12# 1# 0#

Margaret# 10# 0# 1#

Frank# 9# 1# 1#

Jill# 13# 0# 0#

Leon# 10# 1# 0#

Sarah# 12# 0# 0#

Gena# 8# 0# 0#

Patrick# 5# 1# 1#…+

L(h)#=#E(x,y)~P(x,y)[#f(h(x),y)#]###GeneralizaHon+Error:+

h(x)+y+

Person+ Age+ Male?+ Height+>+55”+

Alice# 14# 0# 1#

Bob# 10# 1# 1#

Carol# 13# 0# 1#

Dave# 8# 1# 0#

Erin# 11# 0# 0#

Frank# 9# 1# 1#

Gena# 8# 0# 0#

Person+ Age+ Male?+ Height+>+55”+

James# 11# 1# 1#

Jessica# 14# 0# 1#

Alice# 14# 0# 1#

Amy# 12# 0# 1#

Bob# 10# 1# 1#

Xavier# 9# 1# 0#

Cathy# 9# 0# 1#

Carol# 13# 0# 1#

Eugene# 13# 1# 0#

Rafael# 12# 1# 1#

Dave# 8# 1# 0#

Peter# 9# 1# 0#

Henry# 13# 1# 0#

Erin# 11# 0# 0#

Rose# 7# 0# 0#

Iain# 8# 1# 1#

Paulo# 12# 1# 0#

Margaret# 10# 0# 1#

Frank# 9# 1# 1#

Jill# 13# 0# 0#

Leon# 10# 1# 0#

Sarah# 12# 0# 0#

Gena# 8# 0# 0#

Patrick# 5# 1# 1#…+

L(h)#=#E(x,y)~P(x,y)[#f(h(x),y)#]###GeneralizaHon+Error:+

h(x)+y+

Person+ Age+ Male?+ Height+>+55”+

Alice# 14# 0# 1#

Bob# 10# 1# 1#

Carol# 13# 0# 1#

Dave# 8# 1# 0#

Erin# 11# 0# 0#

Frank# 9# 1# 1#

Gena# 8# 0# 0#

Person+ Age+ Male?+ Height+>+55”+

James# 11# 1# 1#

Jessica# 14# 0# 1#

Alice# 14# 0# 1#

Amy# 12# 0# 1#

Bob# 10# 1# 1#

Xavier# 9# 1# 0#

Cathy# 9# 0# 1#

Carol# 13# 0# 1#

Eugene# 13# 1# 0#

Rafael# 12# 1# 1#

Dave# 8# 1# 0#

Peter# 9# 1# 0#

Henry# 13# 1# 0#

Erin# 11# 0# 0#

Rose# 7# 0# 0#

Iain# 8# 1# 1#

Paulo# 12# 1# 0#

Margaret# 10# 0# 1#

Frank# 9# 1# 1#

Jill# 13# 0# 0#

Leon# 10# 1# 0#

Sarah# 12# 0# 0#

Gena# 8# 0# 0#

Patrick# 5# 1# 1#…+

L(h)#=#E(x,y)~P(x,y)[#f(h(x),y)#]###GeneralizaHon+Error:+

h(x)+y+

TrainingS’

ValidationV’H={2000modelstrainedusingS’}

h(x)=h1(x)+h2(x)+…+hn(x)MaintainensemblemodelascombinationofH:

AddmodelfromHthatmaximizesperformanceonV’

+hn+1(x)

Repeat

S

Denoteashn+1

ModelsaretrainedonS’EnsemblebuilttooptimizeV’

Page 38: Lecture 19 UN EN - GitHub PagesEnsemble methods •Combine different models together to •Minimize variance •Bagging •Random Forests •Minimize bias •Functional Gradient Descent

Summary

38

Method MinimizeBias? MinimizeVariance? Other Comments

Bagging Complexmodelclass.(DeepDTs)

Bootstrap aggregation(resamplingtrainingdata)

Does notworkforsimplemodels.

RandomForests

Complexmodelclass.(DeepDTs)

Bootstrapaggregation+bootstrappingfeatures

Onlyfordecisiontrees.

GradientBoosting(AdaBoost)

Optimizetrainingperformance.

Simplemodelclass.(ShallowDTs)

Determineswhichmodeltoaddatrun-time.

EnsembleSelection

Optimize validationperformance

Optimizevalidationperformance

Pre-specified dictionaryofmodelslearnedontrainingset.


Bagging Films En

Bagging Films En

Documents
Vacuum Bagging Techniques - Jamestown Distributors...Vacuum Bagging Techniques A guide to the principles and practical application of vacuum bagging for ... differential (clamping

Vacuum Bagging Techniques - Jamestown Distributors...Vacuum Bagging Techniques A guide to the principles and practical application of vacuum bagging for ... differential (clamping

Documents
Download Vacuum Bagging Techniques

Download Vacuum Bagging Techniques

Documents
Bagging in Textiles

Bagging in Textiles

Documents
Newton’s Method - CMU Statisticsryantibs/convexopt/lectures/newton.pdf · Newton’s method interpretation Recall the motivation for gradient descent step at x: we minimize the

Newton’s Method - CMU Statisticsryantibs/convexopt/lectures/newton.pdf · Newton’s method interpretation Recall the motivation for gradient descent step at x: we minimize the

Documents
Sand bagging

Sand bagging

Business
INFO-4604, Applied Machine Learning University of Colorado ... › classes › INFO-4604 › fa17 › ... · Learning We can use gradient descent to minimize the negative log-likelihood,

INFO-4604, Applied Machine Learning University of Colorado ... › classes › INFO-4604 › fa17 › ... · Learning We can use gradient descent to minimize the negative log-likelihood,

Documents
Bonus bagging betting system

Bonus bagging betting system

Entertainment & Humor
Bagging Equipment | Bag Filling Machines - Choice Bagging

Bagging Equipment | Bag Filling Machines - Choice Bagging

Documents
Boosting e Bagging

Boosting e Bagging

Documents
Poly Bagging Supplies and Equipment

Poly Bagging Supplies and Equipment

Business
Vacuum Bagging Basics - Diane Selkirk Vacuum Bagging Basics.pdf · Vacuum Bagging Basics Vacuum bagging small fiberglass panels provides a higher ... double as comfortable foredeck

Vacuum Bagging Basics - Diane Selkirk Vacuum Bagging Basics.pdf · Vacuum Bagging Basics Vacuum bagging small fiberglass panels provides a higher ... double as comfortable foredeck

Documents
(Sub)gradient Descent - UMD...•Gradient descent –A generic algorithm to minimize objective functions –Works well as long as functions are well behaved (ie convex) –Subgradient

(Sub)gradient Descent - UMD...•Gradient descent –A generic algorithm to minimize objective functions –Works well as long as functions are well behaved (ie convex) –Subgradient

Documents
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE ...cmp.felk.cvut.cz/~zimmerk/zimmermann-TPAMI.pdf · Lucas and Kanade [1], [2] use the steepest descent optimization to minimize

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE ...cmp.felk.cvut.cz/~zimmerk/zimmermann-TPAMI.pdf · Lucas and Kanade [1], [2] use the steepest descent optimization to minimize

Documents
Bagging and Forecasting in Nonlinear Dynamic Models Mari ... · introduces bagging in the eld of machine learning in random sample settings. The bagging method uses the bootstrap

Bagging and Forecasting in Nonlinear Dynamic Models Mari ... · introduces bagging in the eld of machine learning in random sample settings. The bagging method uses the bootstrap

Documents
Descent and Essential Descent Spectrum of Linear Relations · Descent and Essential Descent Spectrum ... ezzeddine.chafai@ipeis.rnu.tn, ... we study the descent spectrum and the essential

Descent and Essential Descent Spectrum of Linear Relations · Descent and Essential Descent Spectrum ... [email protected], ... we study the descent spectrum and the essential

Documents
Vacuum Bagging Techniques (pdf) - WEST · PDF fileVacuum Bagging Techniques A guide to the principles and practical application of vacuum bagging for laminating composite materials

Vacuum Bagging Techniques (pdf) - WEST · PDF fileVacuum Bagging Techniques A guide to the principles and practical application of vacuum bagging for laminating composite materials

Documents
Rice Bagging Machine

Rice Bagging Machine

Documents
Automatic Bagging Systems

Automatic Bagging Systems

Technology
DIY Vacuum Bagging Basics

DIY Vacuum Bagging Basics

Documents
Bagging, Boosting

Bagging, Boosting

Documents
Newton’s Method - CMU Statisticsryantibs/convexopt-F15/lectures/14-newton.pdf · Newton’s method interpretation Recall the motivation for gradient descent step at x: we minimize

Newton’s Method - CMU Statisticsryantibs/convexopt-F15/lectures/14-newton.pdf · Newton’s method interpretation Recall the motivation for gradient descent step at x: we minimize

Documents
GUNUNG CIREMAI - Gunung Bagging

GUNUNG CIREMAI - Gunung Bagging

Documents
SB2b Statistical Machine Learning Bagging Decision Trees ...flaxman/HT17_lecture14.pdf · Tree Ensembles Bagging Variance Reduction in Bagging Suppose, in an ideal world, our estimators

SB2b Statistical Machine Learning Bagging Decision Trees ...flaxman/HT17_lecture14.pdf · Tree Ensembles Bagging Variance Reduction in Bagging Suppose, in an ideal world, our estimators

Documents
Social Bagging 101

Social Bagging 101

Technology
OBSERVATIONS ON BAGGING - Statistics Departmentstat.wharton.upenn.edu/~buja/PAPERS/sinica-bagging-buja-stuetzle.pdf · 1 OBSERVATIONS ON BAGGING Andreas Buja and Werner Stuetzle University

OBSERVATIONS ON BAGGING - Statistics Departmentstat.wharton.upenn.edu/~buja/PAPERS/sinica-bagging-buja-stuetzle.pdf · 1 OBSERVATIONS ON BAGGING Andreas Buja and Werner Stuetzle University

Documents
Net Bagging Brochure - TWSPL

Net Bagging Brochure - TWSPL

Documents
Contract-Bagging …

Contract-Bagging …

Documents
Random generalized linear model: a highly accurate and ... · Bagging • Bagging = Bootstrap aggregating • Nonparametric Bootstrap (standard bagging): • Bag is drawn at random

Random generalized linear model: a highly accurate and ... · Bagging • Bagging = Bootstrap aggregating • Nonparametric Bootstrap (standard bagging): • Bag is drawn at random

Documents
Tunnel Finisher & Bagging Machines · Automatic Bagging Machines for Hanging Garments The automatic bagging machine SPEED-PACK from VEIT BRISAY guarantees quick, reliable and cost-effective

Tunnel Finisher & Bagging Machines · Automatic Bagging Machines for Hanging Garments The automatic bagging machine SPEED-PACK from VEIT BRISAY guarantees quick, reliable and cost-effective

Documents