Averaging weak classifiers

Dechang Chen; Jian Liu

doi:10.1007/3-540-48219-9_12

Averaging weak classifiers

Dechang Chen, Jian Liu

Epidemiology and Biostatistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present a learning algorithm for two-class pattern recognition. It is based on combining a large number of weak classifiers. The weak classifiers are produced independently with diversity. And they are combined through a weighted average, weighted exponentially with respect to their apparent errors on the training data. Experimental results are also given.

Original language	English
Title of host publication	Multiple Classifier Systems
Subtitle of host publication	2nd International Workshop, MCS 2001, Proceedings
Editors	Josef Kittler, Fabio Roli
Publisher	Springer Verlag
Pages	119-125
Number of pages	7
ISBN (Print)	3540422846, 9783540422846
DOIs	https://doi.org/10.1007/3-540-48219-9_12
State	Published - 2001
Event	2nd International Workshop on Multiple Classifier Systems, MCS 2001 - Cambridge, United Kingdom Duration: 2 Jul 2001 → 4 Jul 2001

Publication series

Name	Lecture Notes in Computer Science
Volume	2096 2096 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Multiple Classifier Systems, MCS 2001
Country/Territory	United Kingdom
City	Cambridge
Period	2/07/01 → 4/07/01

Access to Document

10.1007/3-540-48219-9_12

Cite this

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title = "Averaging weak classifiers",

abstract = "We present a learning algorithm for two-class pattern recognition. It is based on combining a large number of weak classifiers. The weak classifiers are produced independently with diversity. And they are combined through a weighted average, weighted exponentially with respect to their apparent errors on the training data. Experimental results are also given.",

author = "Dechang Chen and Jian Liu",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 2nd International Workshop on Multiple Classifier Systems, MCS 2001 ; Conference date: 02-07-2001 Through 04-07-2001",

year = "2001",

doi = "10.1007/3-540-48219-9_12",

language = "English",

isbn = "3540422846",

series = "Lecture Notes in Computer Science",

publisher = "Springer Verlag",

pages = "119--125",

editor = "Josef Kittler and Fabio Roli",

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}

Chen, D & Liu, J 2001, Averaging weak classifiers. in J Kittler & F Roli (eds), Multiple Classifier Systems: 2nd International Workshop, MCS 2001, Proceedings. Lecture Notes in Computer Science, vol. 2096 2096 LNCS, Springer Verlag, pp. 119-125, 2nd International Workshop on Multiple Classifier Systems, MCS 2001, Cambridge, United Kingdom, 2/07/01. https://doi.org/10.1007/3-540-48219-9_12

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AU - Chen, Dechang

AU - Liu, Jian

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2001.

PY - 2001

Y1 - 2001

N2 - We present a learning algorithm for two-class pattern recognition. It is based on combining a large number of weak classifiers. The weak classifiers are produced independently with diversity. And they are combined through a weighted average, weighted exponentially with respect to their apparent errors on the training data. Experimental results are also given.

AB - We present a learning algorithm for two-class pattern recognition. It is based on combining a large number of weak classifiers. The weak classifiers are produced independently with diversity. And they are combined through a weighted average, weighted exponentially with respect to their apparent errors on the training data. Experimental results are also given.

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U2 - 10.1007/3-540-48219-9_12

DO - 10.1007/3-540-48219-9_12

M3 - Conference contribution

AN - SCOPUS:84956987651

SN - 3540422846

SN - 9783540422846

T3 - Lecture Notes in Computer Science

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EP - 125

BT - Multiple Classifier Systems

A2 - Kittler, Josef

A2 - Roli, Fabio

PB - Springer Verlag

T2 - 2nd International Workshop on Multiple Classifier Systems, MCS 2001

Y2 - 2 July 2001 through 4 July 2001

ER -

Averaging weak classifiers

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