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Книги з теми "Neural-symbolic"

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Neural-symbolic.

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. Neural-Symbolic Learning Systems. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3.

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2

C, Lamb Luís, and Gabbay Dov M. 1945-, eds. Neural-symbolic cognitive reasoning. Berlin: Springer, 2009.

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3

Pascal, Hitzler, and SpringerLink (Online service), eds. Perspectives of Neural-Symbolic Integration. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.

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4

Hammer, Barbara, and Pascal Hitzler, eds. Perspectives of Neural-Symbolic Integration. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73954-8.

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5

Sun, Ron, and Lawrence A. Bookman, eds. Computational Architectures Integrating Neural And Symbolic Processes. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/b102608.

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6

Garcez, Artur S. D'Avila. Neural-Symbolic Learning Systems: Foundations and Applications. London: Springer London, 2002.

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7

International School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data (2002 Erice, Italy). From synapses to rules: Discovering symbolic rules from neural processed data. New York: Kluwer Academic/Plenum Pub., 2002.

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8

Apolloni, Bruno. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Boston, MA: Springer US, 2002.

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9

Dong, Tiansi. A Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56275-5.

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10

Ruan, Da. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms. Boston, MA: Springer US, 1997.

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11

IEEE International Conference on Fuzzy Systems (2nd 1993 San Francisco, Calif.). Second IEEE International Conference on Fuzzy Systems: San Francisco, California, March 28-April 1, 1993. [New York]: IEEE, 1993.

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12

Ross, Deming, Ilin Roman, and SpringerLink (Online service), eds. Emotional Cognitive Neural Algorithms with Engineering Applications: Dynamic Logic: FromVague to Crisp. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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13

Conference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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14

E, Diday, and Institut national de recherche en informatique et en automatique (France), eds. Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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15

Romania) International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (12th 2010 Timișoara. 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing: Proceedings : SYNASC 2010, 23-26 September 2010, Timișoara, Romania. Los Alamitos, Calif: IEEE Computer Society, 2010.

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16

Romania) International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (9th 2007 Timișoara. Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing: SYNASC 2007 : Timișoara, Romania : September 26-29, 2007 : proceedings. Los Alamitos, Calif: IEEE Computer Society, 2007.

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17

International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (10th 2008 Timișoara, Romania). Proceedings of the 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing: 26-29 September 2008, Timișoara, Romania. Los Alamitos, Calif: IEEE Computer Society, 2009.

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18

Romania) International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (11th 2009 Timișoara. Eleventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing: Proceedings : SYNASC 2009, 26-29 September 2009, Timișoara, Romania. Los Alamitos, Calif: IEEE Computer Society, 2009.

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19

Timișoara, Romania) International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (8th 2006. SYNASC 2006: Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing : proceedings : 26-29 September, 2006, Timisoara, Romania. Los Alamitos, California: IEEE Computer Society, 2007.

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20

Romania) International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (13th 2011 Timișoara. 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing: Proceedings : [SYNASC 2011], Timișoara, Romania, 26-29 September 2011. Edited by Wang Dongming, IEEE Computer Society, and Institute of Electrical and Electronics Engineers. Los Alamitos, Calif: IEEE Computer Society, 2012.

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21

Neural-Symbolic Cognitive Reasoning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-73246-4.

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22

Garcez, Artur S. D'Avila, Dov M. Gabbay, and Luís C. Lamb. Neural-Symbolic Cognitive Reasoning. Springer, 2008.

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23

Neural-Symbolic Learning Systems. Springer, 2002.

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24

Neural-Symbolic Cognitive Reasoning. Springer, 2010.

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25

Hammer, Pascal Hitzler Barbara. Perspectives of Neural-Symbolic Integration. Springer, 2008.

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26

Hammer, Barbara, and Pascal Hitzler. Perspectives of Neural-Symbolic Integration. Springer Berlin / Heidelberg, 2010.

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27

A, Bookman Lawrence, and Sun Ron, eds. Architectures for integrating neural and symbolic processes. Abingdon: Carfax, 1993.

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28

(Editor), Bruno Apolloni, and Franz Kurfess (Editor), eds. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Springer, 2002.

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29

Dong, Tiansi. Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Springer International Publishing AG, 2020.

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30

Dong, Tiansi. Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Springer International Publishing AG, 2021.

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31

1960-, Sun Ron, and Bookman Lawrence A. 1947-, eds. Computational architectures integrating neural and symbolic processes: A perspective on the state of the art. Boston: Kluwer Academic, 1994.

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32

Sun, Ron. Computational Architectures Integrating Neural and Symbolic Processes: A Perspective On The State Of The Art. Springer, 2013.

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33

Sun, Ron, and Lawrence A. Bookman. Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art. Springer London, Limited, 2007.

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34

Boden, Margaret A. 4. Artificial neural networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0004.

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Анотація:

Artificial neural networks (ANNs) are made up of many interconnected units, each one capable of computing only one thing. ANNs have myriad applications, from playing the stock market and monitoring currency fluctuations to recognizing speech or faces. ANNs are parallel-processing virtual machines implemented on classical computers. They are intriguing partly because they are very different from the virtual machines of symbolic AI. Sequential instructions are replaced by massive parallelism, top-down control by bottom-up processing, and logic by probability. ‘Artificial neural networks’ considers the wider implications of ANNs and discusses parallel distributed processing (PDP), learning in neural networks, back-propagation, deep learning, and hybrid systems.

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35

Perlovsky, Leonid, Ross Deming, and Roman Ilin. Emotional Cognitive Neural Algorithms with Engineering Applications : Dynamic Logic: From Vague to Crisp. Springer, 2013.

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36

(Editor), Ron Sun, and Lawrence A. Bookman (Editor), eds. Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art (The Springer International Series in Engineering and Computer Science). Springer, 1994.

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37

The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Fuzzy Logic with C++, Java, Symbolic C++, and Reduce Program. World Scientific Publishing Company, 1999.

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38

Mundy, Peter. A Neural Networks, Information-Processing Model of Joint Attention and Social-Cognitive Development. Edited by Philip David Zelazo. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199958474.013.0010.

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A neural networks approach to the development of joint attention can inform the study of the nature of human social cognition, learning, and symbolic thought process. Joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one’s own attention and the attention of other people. Infant practice with joint attention is both a consequence and an organizer of a distributed and integrated brain network involving frontal and parietal cortical systems. In this chapter I discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances the depth of information processing and encoding beginning in the first year of life. I also propose that with development joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human social cognition and symbolic thinking.

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39

Second IEEE International Conference on Fuzzy Systems: San Francisco, California, March 28-April 1, 1993. IEEE, 1993.

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40

Second IEEE International Conference on Fuzzy Systems: San Francisco, California, March 28-April 1, 1993. IEEE, 1993.

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41

Gebuis, Titia, and Bert Reynvoet. Number Representations and their Relation with Mathematical Ability. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.035.

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Анотація:

In this chapter we review research on the processes that underlie the development of mathematical abilities. It is proposed that numerical deficiencies might arise from domain specific problems. The approximate number system that supports reasoning with non-symbolic numbers, on the one hand, and the symbolic number system on the other hand were put forth as possible candidates. To gain insight into the two different systems, we will describe the development of non-symbolic and symbolic number processing and introduce the two main theories about numerical deficiencies: the approximate number system and the access deficit hypothesis. The paradigms used to study both accounts differ in several ways and are of importance for research on the relation between non-symbolic and symbolic number and mathematical abilities. Then, we will review how the studies investigating both accounts relate to two different sets of developmental models that describe the neural representation of number.

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42

Elements of Causal Inference. The MIT Press, 2017.

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43

Rolls, Edmund T. The Neuroscience of Purpose, Meaning, and Morals. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190460723.003.0005.

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In Chapter 5, Edmund T. Rolls builds on evidence and theories he developed elsewhere about the neural base of emotions and explores what they can tell us about purpose, meaning, and morals. He argues that meaning can be achieved by neural representations not only if these representations have mutual information with objects and events in the world, but also by virtue of the goals of the “selfish” genes and of the individual reasoner. This, he proposes, provides a means for even symbolic representations to be grounded in the world. He concludes by arguing that morals can be considered as principles that are underpinned by (the sometimes different) biological goals specified by the genes and by the reasoning (rational) system. Given that what is “natural” does not correspond to what is “right,” he argues that these conflicts within and between individuals can be addressed by a social contract.

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44

(Editor), Takeshi Furuhashi, Shun'Ichi Tano (Editor), and Hans-Arno Jacobsen (Editor), eds. Deep Fusion of Computational and Symbolic Processing. Physica-Verlag Heidelberg, 2001.

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45

Sun, Ron, and Frederic Alexandre. Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Taylor & Francis Group, 2013.

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46

Sun, Ron, and Frederic Alexandre. Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Taylor & Francis Group, 2013.

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47

Sun, Ron, and Frederic Alexandre. Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Taylor & Francis Group, 2013.

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48

Sun, Ron, and Frederic Alexandre. Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Taylor & Francis Group, 2013.

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49

Thagard, Paul. Brain-Mind. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190678715.001.0001.

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Анотація:

Minds enable people to perceive, imagine, solve problems, understand, learn, speak, reason, create, and be emotional and conscious. Competing explanations of how the mind works have identified it as soul, computer, brain, dynamical system, or social construction. This book explains minds in terms of interacting mechanisms operating at multiple levels, including the social, mental, neural, and molecular. Brain–Mind presents a unified, brain-based theory of cognition and emotion with applications to the most complex kinds of thinking, right up to consciousness and creativity. Unification comes from systematic application of Chris Eliasmith’s powerful new Semantic Pointer Architecture, a highly original synthesis of neural network and symbolic ideas about how the mind works. The book shows the relevance of semantic pointers to a full range of important kinds of mental representations, from sensations and imagery to concepts, rules, analogies, and emotions. Neural mechanisms are used to explain many phenomena concerning consciousness, action, intention, language, creativity, and the self. This book belongs to a trio that includes Mind–Society: From Brains to Social Sciences and Professions and Natural Philosophy: From Social Brains to Knowledge, Reality, Morality, and Beauty. They can be read independently, but together they make up a Treatise on Mind and Society that provides a unified and comprehensive treatment of the cognitive sciences, social sciences, professions, and humanities.

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50

Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Lawrence Erlbaum, 1997.

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