Issues in the Use of Neural Networks in Information Retrieval (Record no. 79092)

000 -LEADER
fixed length control field 03368nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-319-43871-9
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801220925.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160928s2017 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319438719
-- 978-3-319-43871-9
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Iatan, Iuliana F.
245 10 - TITLE STATEMENT
Title Issues in the Use of Neural Networks in Information Retrieval
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2017.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIX, 199 p. 88 illus., 44 illus. in color.
490 1# - SERIES STATEMENT
Series statement Studies in Computational Intelligence,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Mathematical Aspects of Using Neural Approaches for Information Retrieval -- A Fuzzy Kwan- Cai Neural Network for Determining Image Similarity and for the Face Recognition -- Predicting Human Personality from Social Media using a Fuzzy Neural Network -- Modern Neural Methods for Function Approximation -- A Fuzzy Gaussian Clifford Neural Network -- Concurrent Fuzzy Neural Networks -- A New Interval Arithmetic Based Neural Network -- A Recurrent Neural Fuzzy Network.
520 ## - SUMMARY, ETC.
Summary, etc This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality. It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules. Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-43871-9
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2017.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neural networks (Computer science) .
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition systems.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Mathematical Models of Cognitive Processes and Neural Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automated Pattern Recognition.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1860-9503 ;
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-- ZDB-2-ENG
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-- ZDB-2-SXE

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