The Resource Ensemble learning for prediction, Bogdan E. Popescu

Ensemble learning for prediction, Bogdan E. Popescu

Label
Ensemble learning for prediction
Title
Ensemble learning for prediction
Statement of responsibility
Bogdan E. Popescu
Creator
Contributor
Thesis advisor
Subject
Language
eng
Summary
The goal of this dissertation is to study and develop automatic prediction technology that is accurate, fast and interpretable. The focus here is on decision tree ensembles methods and extensions. Characteristics of popular ensemble methods such as bagging, random forests and boosting are examined and leveraged to create new predictive methodology. The classic ensembles are integrated in an unifying paradigm, the Important Sampled Learning Ensembles. This framework explains some of the properties of these ensembles and suggests modifications that can significantly enhance their accuracy while dramatically improving their computational performance. The ISLES are two-stage algorithms having at the front-end a base learners ensemble generation routine followed by post-processing algorithms that perform a fast gradient directed regularized fit for regression, robust regression and classification. The post-processing algorithms developed here can also serve as a stand-alone toolkit for fitting large linear systems. Decision tree ensembles can generate rules that are fit together with the gradient directed regularized linear algorithms, leading to accurate and interpretable RuleFit models. ISLE and RuleFit are flexible methodologies, able to automatically handle non-linearities and interactions, mixtures of categorical and continuous variables with missing data, as well as feature selection
Cataloging source
MUU
http://library.link/vocab/creatorName
Popescu, Bogdan E
Degree
Ph. D.
Dissertation year
2004.
Granting institution
Stanford University
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorName
  • Friedman, J. H.
  • Stanford University
http://library.link/vocab/subjectName
  • Predictive control
  • Operations research
  • Decision trees
Target audience
specialized
Label
Ensemble learning for prediction, Bogdan E. Popescu
Instantiates
Publication
Note
  • Submitted to the Department of Statistics
  • Jerome H. Friedman, Advisor
  • Typescript
Bibliography note
Includes bibliographical references
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
813830031
Dimensions
cm.
Extent
xvii, 166 leaves
Form of item
regular print reproduction
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
Reproduction note
Photocopy.
System control number
(OCoLC)813830031
Label
Ensemble learning for prediction, Bogdan E. Popescu
Publication
Note
  • Submitted to the Department of Statistics
  • Jerome H. Friedman, Advisor
  • Typescript
Bibliography note
Includes bibliographical references
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
813830031
Dimensions
cm.
Extent
xvii, 166 leaves
Form of item
regular print reproduction
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
Reproduction note
Photocopy.
System control number
(OCoLC)813830031

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