Issues in the Use of Neural Networks in Information Retrieval

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 ne...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Iatan, Iuliana F. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
Σειρά:Studies in Computational Intelligence, 661
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Iatan, Iuliana F.  |e author. 
245 1 0 |a Issues in the Use of Neural Networks in Information Retrieval  |h [electronic resource] /  |c by Iuliana F. Iatan. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2017. 
300 |a XIX, 199 p. 88 illus., 44 illus. in color.  |b online resource. 
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490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 661 
505 0 |a 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 |a 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. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Pattern recognition. 
650 0 |a Neural networks (Computer science). 
650 0 |a Computational intelligence. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Mathematical Models of Cognitive Processes and Neural Networks. 
650 2 4 |a Pattern Recognition. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319438702 
830 0 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 661 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-43871-9  |z Full Text via HEAL-Link 
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950 |a Engineering (Springer-11647)