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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling (Record no. 11991)

000 -LEADER
fixed length control field 02736nam a22004215i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140310143352.0
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 120720s2013 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642307522
978-3-642-30752-2
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.382
Edition number 23
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2013.
912 ## -
-- ZDB-2-ENG
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Salazar, Addisson.
Relator term author.
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
Medium [electronic resource] /
Statement of responsibility, etc by Addisson Salazar.
300 ## - PHYSICAL DESCRIPTION
Extent XXII, 185 p. 73 illus.
Other physical details online resource.
440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Springer Theses, Recognizing Outstanding Ph.D. Research,
International Standard Serial Number 2190-5053 ;
Volume number/sequential designation 4
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction -- ICA and ICAMM Methods -- Learning Mixtures of Independent Component Analysers -- Hierarchical Clustering from ICA Mixtures -- Application of ICAMM to Impact-Echo Testing -- Cultural Heritage Applications: Archaeological Ceramics and Building Restoration -- Other Applications: Sequential Dependence Modelling and Data Mining -- Conclusions.
520 ## - SUMMARY, ETC.
Summary, etc A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Optical pattern recognition.
Topical term or geographic name as entry element Physics.
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Signal, Image and Speech Processing.
Topical term or geographic name as entry element Pattern Recognition.
Topical term or geographic name as entry element Complexity.
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 9783642307515
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-30752-2
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
2014-04-02AUM Main Library2014-04-02 2014-04-02 E-Book   AUM Main Library621.382

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