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Ensemble Machine Learning (Record no. 10321)

000 -LEADER
fixed length control field 02786nam a22004215i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140310143333.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120216s2012 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781441993267
978-1-4419-9326-7
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
264 #1 -
-- Boston, MA :
-- Springer US,
-- 2012.
912 ## -
-- ZDB-2-ENG
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Zhang, Cha.
Relator term editor.
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title Ensemble Machine Learning
Medium [electronic resource] :
Remainder of title Methods and Applications /
Statement of responsibility, etc edited by Cha Zhang, Yunqian Ma.
300 ## - PHYSICAL DESCRIPTION
Extent VIII, 329p. 84 illus.
Other physical details online resource.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction of Ensemble Learning -- Boosting Algorithms: Theory, Methods and Applications -- On Boosting Nonparametric Learners -- Super Learning -- Random Forest -- Ensemble Learning by Negative Correlation Learning -- Ensemble Nystrom Method -- Object Detection -- Ensemble Learning for Activity Recognition -- Ensemble Learning in Medical Applications -- Random Forest for Bioinformatics.
520 ## - SUMMARY, ETC.
Summary, etc It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Computer science.
Topical term or geographic name as entry element Data mining.
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Computational Intelligence.
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
Topical term or geographic name as entry element Computer Science, general.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ma, Yunqian.
Relator term editor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9781441993250
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4419-9326-7
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type E-Book
Copies
Price effective from Permanent location Date last seen Not for loan Date acquired Source of classification or shelving scheme Koha item type Damaged status Lost status Withdrawn status Current location Full call number
2014-03-28AUM Main Library2014-03-28 2014-03-28 E-Book   AUM Main Library006.3

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