Issues in the Use of Neural Networks in Information Retrieval

Nonfiction, Science & Nature, Mathematics, Applied, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Issues in the Use of Neural Networks in Information Retrieval by Iuliana F. Iatan, Springer International Publishing
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Author: Iuliana F. Iatan ISBN: 9783319438719
Publisher: Springer International Publishing Publication: September 28, 2016
Imprint: Springer Language: English
Author: Iuliana F. Iatan
ISBN: 9783319438719
Publisher: Springer International Publishing
Publication: September 28, 2016
Imprint: Springer
Language: English

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.

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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.

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