國立高雄大學圖資館 |

Language: English

Back

Ensemble learning for prediction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Ensemble learning for prediction
Author:	Popescu, Bogdan E.
Description:	166 p.
Notes:	Adviser: Jerome H. Friedman.
Notes:	Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1942.
Contained By:	Dissertation Abstracts International65-04B.
Subject:	Statistics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3128457
ISBN:	0496757199

Ensemble learning for prediction
Popescu, Bogdan E.

Ensemble learning for prediction [electronic resource] - 166 p.

Adviser: Jerome H. Friedman.

Thesis (Ph.D.)--Stanford University, 2004.

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.

ISBN: 0496757199Subjects--Topical Terms:

182057
Statistics.

Ensemble learning for prediction
LDR:02388nmm _2200289 _450 001 162611
005 20051017073504.5
008 230606s2004 eng d
020 $a 0496757199
035 $a 00149112
035 $a 162611
040 $a UnM $c UnM
100 0 $a Popescu, Bogdan E. $3 227755
245 1 0 $a Ensemble learning for prediction $h [electronic resource]
300 $a 166 p.
500 $a Adviser: Jerome H. Friedman.
500 $a Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1942.
502 $a Thesis (Ph.D.)--Stanford University, 2004.
520 # $a 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.
590 $a School code: 0212.
650 # 0 $a Statistics. $3 182057
650 # 0 $a Computer Science. $3 212513
650 # 0 $a Operations Research. $3 227148
690 $a 0463
690 $a 0796
690 $a 0984
710 0 # $a Stanford University. $3 212607
773 0 # $g 65-04B. $t Dissertation Abstracts International
790 $a 0212
790 1 0 $a Friedman, Jerome H., $e advisor
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://libsw.nuk.edu.tw/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3128457 $z http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3128457