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Titlebook: Issues in the Use of Neural Networks in Information Retrieval; Iuliana F. Iatan Book 2017 Springer International Publishing Switzerland 20

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發(fā)表于 2025-3-21 18:38:14 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Issues in the Use of Neural Networks in Information Retrieval
編輯Iuliana F. Iatan
視頻videohttp://file.papertrans.cn/476/475905/475905.mp4
概述Highlights the ability of Neural Networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR).Defines a new neural-network-based method for learning image similar
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Issues in the Use of Neural Networks in Information Retrieval;  Iuliana F. Iatan Book 2017 Springer International Publishing Switzerland 20
描述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..
出版日期Book 2017
關(guān)鍵詞Computational Intelligence; Information Retrieval; Fuzzy Gaussian Neural Network; Fuzzy Clifford Gaussi
版次1
doihttps://doi.org/10.1007/978-3-319-43871-9
isbn_softcover978-3-319-82930-2
isbn_ebook978-3-319-43871-9Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer International Publishing Switzerland 2017
The information of publication is updating

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發(fā)表于 2025-3-21 23:51:22 | 只看該作者
Modern Neural Methods for Function Approximation,f hierarchical neural-fuzzy systems for function approximation on discrete spaces, 1:29–41, 2005, [.]) strong research in the past years. Application approaches with their solid results ilustrate that such approximations by the NNs have remarkable accuracy, especially by feedforward neural networks
板凳
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地板
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發(fā)表于 2025-3-22 09:32:57 | 只看該作者
A New Interval Arithmetic-Based Neural Network,4, [.] differs from the other fuzzy variants of the nonlinear perceptron, where the fuzzy numbers are represented by membership values. In the case of FNPALS, the fuzzy numbers are represented through the alpha level sets.
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發(fā)表于 2025-3-22 14:20:54 | 只看該作者
1860-949X a new neural-network-based method for learning image similarThis 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
7#
發(fā)表于 2025-3-22 21:06:03 | 只看該作者
A Recurrent Neural Fuzzy Network,he layers of neurons of these networks and within the same layer, too. The aim of this chapter is to describe a Recurrent Fuzzy Neural Network (RFNN) model, whose learning algorithm is based on the Improved Particle Swarm Optimization (IPSO) method.
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發(fā)表于 2025-3-23 03:23:12 | 只看該作者
,A Fuzzy Kwan–Cai Neural Network for Determining Image Similarity and for the Face Recognition,ong and Gong, Image Vis Comput, 29:774–786, 2011 [.]), (Chowhan, Int J Comput Electr Eng, 3(5):743–747, 2011 [.]) and for supervised classification (Hariri, Shokouhi and Mozayani, Iran J Electr Electron Eng, 4(3):79–93, 2008 [.]).
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發(fā)表于 2025-3-23 07:45:49 | 只看該作者
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