000 -LEADER |
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02786nam a22004215i 4500 |
003 - CONTROL NUMBER IDENTIFIER |
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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 |
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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 |