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Books on the topic 'Neural network classification algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Todd, Ian K. A new neural network algorithm for classification problems. The author], 1999.

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2

Amezcua, Jonathan, Patricia Melin, and Oscar Castillo. New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73773-7.

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3

Errington, Phillip Anthony. Application of neural network models to chromosome classification. University of Manchester, 1995.

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4

Wichert, Terry S. Feature based neural network acoustic transient signal classification. Naval Postgraduate School, 1993.

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5

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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6

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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7

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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8

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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9

Bennett, Richard Campbell. Classification of underwater signals using a back-propagation neural network. Naval Postgraduate School, 1997.

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10

Neelanarayanan, ed. Wavelet Based Decomposition and Neural Network Classification for Melanoma Diagnosis. Association of Scientists, Developers and Faculties, 2014.

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11

Hong, X. A Givens rotation based fast backward elimination algorithm for RBF neural network pruning. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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12

Lim, Chee Peng. On-line pattern classification with multiple neural network systems:an experimental study. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1996.

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13

Lim, Chee Peng. Application of autonomous neural network systems to medical pattern classification tasks. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1996.

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14

Lim, Chee Peng. A Multiple neural network architecture for sequential evidence aggregation and incomplete data classification. Univeristy of Sheffield, Dept. of Automatic Control and Systems Engineering, 1997.

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15

Joseph, Downs, ed. Application of the fuzzy ARTMAP neural network model to medical pattern classification tasks. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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16

Ding, Yao, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, and Yijun Zhang. Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8009-9.

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17

Wang, Jun. A Bayesian classifier based on a deterministic annealing neural network for aircraft fault classification. Human Resources Directorate, Logistics Research Division, U.S. Air Force Armstrong Laboratory, 1997.

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18

Ann, Frazier, and Geological Survey (U.S.). National Mapping Division, eds. Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features. U.S. Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1995.

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19

Lemeshewsky, George. Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features. U.S. Geological Survey, 1995.

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20

P, Dhawan Atam, Meyer Claudia M, and United States. National Aeronautics and Space Administration., eds. Genetic algorithm based input selection for a neural network function approximator with application to SSME health monitoring. National Aeronautics and Space Administration, 1991.

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21

Malpica, Oscar. Classification of aerosol lams mass spectra using an adaptive resonance theory based neural network (ART-2A). National Library of Canada, 2002.

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22

Clarke, Gerald. Classification of a white king and white pawn, versus a black king chess end game, using a multi-layered neural network. The Author], 1995.

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23

Amezcua, Jonathan. New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic. Springer, 2018.

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24

Fletcher, Justin Barrows Swore. A constructive approach to hybrid architectures for machine learning. 1994.

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25

Neural Network Classification of Environmental Samples. Storming Media, 1996.

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26

Signal Classification Using The Mean Separator Neural Network. Storming Media, 2000.

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27

Modular learning in neural networks: A modularized approach to neural network classification. Wiley, 1992.

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28

Tandon, Neha. Novel Approach for Drug Discovery Using Neural Network Back Propagation Algorithm. GRIN Verlag GmbH, 2018.

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29

Classification of Underwater Signals Using a Back-Propagation Neural Network. Storming Media, 1997.

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30

Parallel Implementation of an Artificial Neural Network Integrated Feature and Architecture Selection Algorithm. Storming Media, 1998.

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31

Statistical methods and neural network approaches for classification of data from multiple sources. Laboratory for Applications of Remote Sensing, 1990.

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32

Intelligent information retrieval using an inductive learning algorithm and a back-propagation neural network. University Microfilms International, 1995.

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33

Neural Network Implementation of A F 14 Battle Management Fusion Algorithm Rule Base/As A223981. Natl Technical Information, 1990.

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34

Sharma, Er Anshul, and Divya Gaba. Comparative Analysis of Classification and Detection of Oil Spill Using Artificial Neural Network. Independently Published, 2018.

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35

Cloud classification in polar and desert regions and smoke classification from biomass burning using a hierarchical neural network. National Aeronautics and Space Administration, 1996.

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36

Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features. U.S. Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1995.

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37

Al-Haddad, Luan Marie. Neural network techniques for the identification and classification of marine phytoplankton from flow cytometric data. 2001.

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38

National Aeronautics and Space Administration (NASA) Staff. Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression. Independently Published, 2018.

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39

Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic

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40

Piccinini, Gualtiero. Computationalism. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0010.

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The introduction of the concept of computation in cognitive science is discussed in this article. Computationalism is usually introduced as an empirical hypothesis that can be disconfirmed. Processing information is surely an important aspect of cognition so if computation is information processing, then cognition involves computation. Computationalism becomes more significant when it has explanatory power. The most relevant and explanatory notion of computation is that associated with digital computers. Turing analyzed computation in terms of what are now called Turing machines that are the k

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41

Aziz, Nazrina, Syariza Abdul-Rahman, and Norhaslina Zainal Abidin, eds. Recent Applications in Quantitative Methods and Information Technology. UUM Press, 2019. http://dx.doi.org/10.32890/9789672210269.

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This book is a guide for researchers who are involved in statistical, mathematical, information technology and decision science analyses. The purpose of the book is to allow readers to get research ideas on a wide range of topics, such as sampling plans, capital budgeting, completion time in production line, searching pattern for mobile cache replacement policy, home security system with biometric finger print and web service technology. The analyses in each chapter are explained in detail with samples of real applications in daily life to assist readers to appreciate theoretical, algorithm an

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42

Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic

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