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Relevant bibliographies by topics / Neural networks (Computer science) Computational learning theory / Books

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Books on the topic 'Neural networks (Computer science) Computational learning theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Virkumar, Vazirani Umesh, ed. An introduction to computational learning theory. MIT Press, 1994.

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2

White, Halbert. Artificial neural networks: Approximation and learning theory. Blackwell, 1992.

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3

Neal, Radford M. Bayesian learning for neural networks. Springer, 1996.

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4

Neal, Radford M. Bayesian learning for neural networks. University of Toronto, Dept. of Computer Science, 1995.

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5

Golès, E. Neural and Automata Networks: Dynamical Behavior and Applications. Springer Netherlands, 1990.

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6

Learning Bayesian networks. Prentice Hall, 2003.

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7

Neapolitan, Richard E. Learning Bayesian networks. Pearson Prentice Hall, 2004.

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8

Learning and generalisation: With applications to neural networks. 2nd ed. Springer, 2003.

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9

Suresh, Sundaram. Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013.

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10

Niyogi, Partha. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar. Springer US, 1998.

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11

Neural network design and the complexity of learning. MIT Press, 1990.

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12

Wang, Cong. Deterministic learning theory for identification, control, and recognition. CRC Press, 2009.

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13

Jang, Jyh-Shing Roger. Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice Hall, 1997.

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14

Approximation methods for efficient learning of Bayesian networks. IOS Press, 2008.

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15

Vidyasagar, M. A theory of learning and generalization: With applications to neural networks and control systems. Springer, 1997.

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16

The informational complexity of learning: Perspectives on neural networks and generative grammar. Kluwer Academic Publishers, 1998.

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17

Jensen, Finn V. An introduction to Bayesian networks. Springer, 1996.

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18

Jensen, Finn V. An introduction to Bayesian networks. UCL Press, 1996.

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19

Neural control engineering: The emerging intersection between control theory and neuroscience. MIT Press, 2012.

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20

International, Symposium on Synergetics (1987 Schloss Elmau Bavaria). Computational systems--natural and artificial: Proceedings of the International Symposium on Synergetics at Schloss Elmau, Bavaria, May 4-9, 1987. Springer-Verlag, 1987.

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21

American Association for Artificial Intelligence., IEEE Computer Society, IEEE Neural Networks Society, and IEEE Robotics and Automation Council., eds. ICDL 2002: The 2nd International Conference on Development and Learning : proceedings :12-15 June 2002, Cambridge, Massachusetts, USA. IEEE Computer Society, 2002.

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22

Joan, Cabestany, Prieto A. 1945-, and Sandoval Francisco, eds. Computational intelligence and bioinspired systems: 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltru, Barcelona, Spain, June 8-10, 2005 ; proceedings. Springer, 2005.

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23

Tulupʹev, A. L. Baĭesovskie seti: Logiko-veroi︠a︡tnostnyĭ podkhod. "Nauka", 2006.

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24

E, Nicholson Ann, ed. Bayesian artificial intelligence. Chapman & Hall/CRC, 2004.

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25

Filip, Mulier, ed. Learning from data: Concepts, theory, and methods. Wiley, 1998.

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26

Learning from data: Concepts, theory, and methods. 2nd ed. Wiley-Interscience, 2008.

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27

Minsky, Marvin Lee. Perceptrons: An Introduction to Computational Geometry. MIT Press, 1988.

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28

Mars, P. Learning algorithms: Theory and applications in signal processing, control, and communications. CRC Press, 1996.

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29

Bayesian networks and decision graphs. Springer, 2001.

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30

Leigh, J. R. Control Theory. 2nd ed. IET, 2004.

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31

E, Nicholson Ann, ed. Bayesian artificial intelligence. 2nd ed. CRC Press, 2011.

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32

Schütze, Oliver. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Springer Berlin Heidelberg, 2013.

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33

Haussler, David. Probably Approximately Correct Learnability Theory (The Stanford Computer Science Video Journal : Artificial Intelligence Research L). Morgan Kaufmann Pub, 1993.

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34

Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation). Springer, 2006.

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35

Supervised Learning With Complexvalued Neural Networks. Springer, 2012.

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36

(Editor), Geoffrey Hinton, and Terrence J. Sejnowski (Editor), eds. Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience). The MIT Press, 1999.

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37

R, Gabriel Michael, and Moore John, eds. Learning and computational neuroscience: Foundations of adaptive networks. MIT Press, 1990.

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38

Aizenberg, Igor, Naum N. Aizenberg, and Joos P. L. Vandewalle. Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer, 2000.

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39

Magnussen, Holger. Discrete-time cellular neural networks: Theory and global learning algorithms. 1995.

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40

Sundararajan, Narasimhan, Sundaram Suresh, and Ramasamy Savitha. Supervised Learning with Complex-valued Neural Networks. Springer, 2014.

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41

Sundararajan, Narasimhan, Sundaram Suresh, and Ramasamy Savitha. Supervised Learning with Complex-valued Neural Networks. Springer, 2012.

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42

Rao, C. R., and Venu Govindaraju. Machine Learning: Theory and Applications. Elsevier Science & Technology Books, 2013.

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43

(Editor), Michael Gabriel, and John Moore (Editor), eds. Learning and Computational Neuroscience: Foundations of Adaptive Networks (Bradford Books). The MIT Press, 1991.

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44

(Editor), Ke Chen, and Lipo Wang (Editor), eds. Trends in Neural Computation (Studies in Computational Intelligence). Springer, 2006.

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45

Analyzing Neural Time Series Data: Theory and Practice. The MIT Press, 2014.

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46

Judd, J. Stephen, and Robert Hanna. Neural Network Design and the Complexity of Learning. MIT Press, 1990.

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47

Judd, J. Stephen. Neural Network Design and the Complexity of Learning. MIT Press, 2018.

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48

Supervised Sequence Labelling With Recurrent Neural Networks. Springer, 2012.

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49

Cherkassky, Vladimir, and Filip M. Mulier. Learning from Data: Concepts, Theory, and Methods. Wiley & Sons, Incorporated, John, 2009.

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50

Cherkassky, Vladimir, and Filip M. Mulier. Learning from Data: Concepts, Theory, and Methods. Wiley & Sons, Incorporated, John, 2007.

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