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Libros sobre el tema "Computational Learning Sciences"

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Autor: Grafiati
Publicado: 7 de julio de 2024

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1

Ashwin, Ram y Leake David B, eds. Goal-driven learning. Cambridge, Mass: MIT Press, 1995.

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2

D, Li Xiaodong Ph, ed. Simulated evolution and learning: 7th international conference, SEAL 2008, Melbourne, Australia, December 7-10, 2008 : proceedings. Berlin: Springer, 2008.

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3

A, Rosenbaum David y Collyer Charles E, eds. Timing of behavior: Neural, psychological, and computational perspectives. Cambridge, Mass: MIT Press, 1998.

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4

Søren, Brunak, ed. Bioinformatics: The machine learning approach. 2a ed. Cambridge, Mass: MIT Press, 2001.

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5

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

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6

Kearns, Michael J. An introduction to computational learning theory. Cambridge, Mass: MIT Press, 1994.

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7

ISICA, 2008 (2008 Wuhan China). Advances in computation and intelligence: Third international symposium, ISICA 2008 : Wuhan, China, December 19-21, 2008 : proceedings. Berlin: Springer, 2008.

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8

ISICA 2007 (2007 Wuhan, China). Advances in computation and intelligence: Second international symposium, ISICA 2007, Wuhan, China, September 21-23, 2007 ; proceedings. Berlin: Springer, 2007.

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9

Lishan, Kang, ed. Advances in computation and intelligence: Third international symposium, ISICA 2008 : Wuhan, China, December 19-21, 2008 : proceedings. Berlin: Springer, 2008.

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10

ISICA 2008 (2008 Wuhan, China). Advances in computation and intelligence: Third international symposium, ISICA 2008 : Wuhan, China, December 19-21, 2008 : proceedings. Berlin: Springer, 2008.

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11

ISICA 2009 (2009 Huangshi Shi, China). Advances in computation and intelligence: 4th International Symposium on Intelligence Computation and Applications, ISICA 2009, Huangshi, China, October 23-25, 2009 : proceedings. Berlin: Springer, 2009.

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12

P, Roychowdhury Vwani, Siu Kai-Yeung 1966- y Orlitsky Alon 1958-, eds. Theoretical advances in neural computation and learning. Boston: Kluwer Academic, 1994.

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13

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

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14

Conference on Computational Learning Theory (14th 2001 Amsterdam, Netherlands). Computational learning theory: 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16-19, 2001 : proceedings. New York: Springer, 2001.

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15

F, Luger George, ed. Computation and intelligence: Collected readings. Menlo Park, Calif: AAAI Press, 1995.

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16

Roychowdhury, Vwani. Theoretical Advances in Neural Computation and Learning. Boston, MA: Springer US, 1994.

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17

Kacprzyk, Janusz, Vasil Sgurev y Mincho Hadjiski. Intelligent systems: From theory to practice. Berlin: Springer Verlag, 2010.

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18

E, Hinton Geoffrey y Sejnowski Terrence J, eds. Unsupervised learning: Foundations of neural computation. Cambridge, Mass: MIT Press, 1999.

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19

Report of a workshop of pedagogical aspects of computational thinking. Washington, D.C: National Academies Press, 2011.

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20

Satapathy, Suresh Chandra, Vikrant Bhateja y Joao Manuel R.S. Tavares. Information and Decision Sciences: Proceedings of the 6th International Conference on FICTA. Springer, 2018.

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21

Satapathy, Suresh Chandra, Vikrant Bhateja, J. R. Mohanty y Joao Manuel R.S. Tavares. Information and Decision Sciences: Proceedings of the 6th International Conference on FICTA. Springer, 2018.

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22

Belgium) NATO Advanced Study Institute on Learning Theory and Practice (2002 : Louvain. Advances in Learning Theory: Methods, Models and Applications (Nato Science Series. Series III, Computer and Systems Sciences, V. 190). IOS Press, 2003.

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23

Engel, Uwe, Anabel Quan-Haase, Sunny Xun Liu y Lars E. Lyberg. Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods. Taylor & Francis Group, 2021.

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24

Engel, Uwe, Anabel Quan-Haase, Sunny Xun Liu y Lars E. Lyberg. Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods. Taylor & Francis Group, 2021.

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25

Engel, Uwe, Anabel Quan-Haase, Sunny Xun Liu y Lars E. Lyberg. Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods. Taylor & Francis Group, 2021.

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26

Dzeroski, Saso y Ljupco Todorovski. Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences. Springer London, Limited, 2007.

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27

Computational discovery of scientific knowledge: Introduction, techniques, and applications in environmental and life sciences. Berlin: Springer, 2007.

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28

Busemeyer, Jerome R., Zheng Wang, James T. Townsend y Ami Eidels, eds. The Oxford Handbook of Computational and Mathematical Psychology. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.001.0001.

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A comprehensive and authoritative review on most important developments in computational and mathematical psychology that have impacted many other fields in past decades. Written in tutorial style by leading scientists in each topic area, with an emphasis on examples and applications. Each chapter is self-contained and aims to engage readers with various levels of modeling experience. The Handbook covers the key developments in elementary cognitive mechanisms (e.g., signal detection, information processing, reinforcement learning), basic cognitive skills (e.g., perceptual judgment, categorization, episodic memory), higher-level cognition (e.g., Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (e.g., Bayesian estimation and other new model comparison methods), and emerging new directions (e.g., neurocognitive modeling, applications to clinical psychology, quantum cognition) in computation and mathematical psychology. The chapters were written for a typical graduate student in virtually any area of psychology, cognitive science, and related social and behavioral sciences, such as consumer behavior and communication. We also expect it to be useful for readers ranging from advanced undergraduate students to experienced faculty members and researchers. Beyond being a handy reference book, it should be beneficial as a textbook for self-teaching, and for graduate level (or advanced undergraduate level) courses in computational and mathematical psychology.
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29

Kirley, Michael, Zbigniew Michalewicz, Xiaodong Li, Mengjie Zhang y Vic Ciesielski. Simulated Evolution and Learning: 7th International Conference, SEAL 2008, Melbourne, Australia, December 7-10, 2008, Proceedings. Springer London, Limited, 2008.

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30

Wang, Tzai-Der, Xufa Wang y Xiaodong Li. Simulated Evolution and Learning: 6th International Conference, SEAL 2006, Hefei, China, October 15-18, 2006, Proceedings. Springer London, Limited, 2006.

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31

(Editor), Tzai-Der Wang, Xiaodong Li (Editor), Shu-Heng Chen (Editor) y Xufa Wang (Editor), eds. Simulated Evolution and Learning: 6th International Conference, SEAL 2006, Hefei, China, October 15-18, 2006, Proceedings (Lecture Notes in Computer Science). Springer, 2006.

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32

(Editor), David A. Rosenbaum y Charles E. Collyer (Editor), eds. Timing of Behavior: Neural, Psychological, and Computational Perspectives. The MIT Press, 1998.

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33

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

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34

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

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35

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

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36

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

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37

Gureckis, Todd M. y Bradley C. Love. Computational Reinforcement Learning. Editado por Jerome R. Busemeyer, Zheng Wang, James T. Townsend y Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.5.

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Reinforcement learning (RL) refers to the scientific study of how animals and machines adapt their behavior in order to maximize reward. The history of RL research can be traced to early work in psychology on instrumental learning behavior. However, the modern field of RL is a highly interdisciplinary area that lies that the intersection of ideas in computer science, machine learning, psychology, and neuroscience. This chapter summarizes the key mathematical ideas underlying this field including the exploration/exploitation dilemma, temporal-difference (TD) learning, Q-learning, and model-based versus model-free learning. In addition, a broad survey of open questions in psychology and neuroscience are reviewed.
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38

Rolls, Edmund T. Brain Computations. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198871101.001.0001.

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The subject of this book is how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed. The book will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics.
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39

Anthony, M. H. G. y N. Biggs. Computational Learning Theory (Cambridge Tracts in Theoretical Computer Science). Cambridge University Press, 1997.

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40

Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.

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41

Handbook of Machine Learning for Computational Optimization. Taylor & Francis Group, 2021.

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42

Kane, Michael, Taylor Arnold y Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.

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43

Kane, Michael, Taylor Arnold y Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.

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44

Kane, Michael, Taylor Arnold y Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.

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45

Kane, Michael, Taylor Arnold y Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.

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46

Kane, Michael, Taylor Arnold y Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2020.

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47

Yadav, Vikash, Parashu Ram Pal y Chuan-Ming Liu, eds. Recent Developments in Artificial Intelligence and Communication Technologies. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97816810896761220101.

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This book is a review of recent artificial intelligence approaches, initiatives and applications in engineering and science fields. It features contributions that highlight the use of techniques such as machine learning, mining engineering, modeling and simulation, and fuzzy logic methods in the fields of communication, networking and information engineering. The collection of chapters should inspire scholars involved in theoretical and applied sciences to contribute to research using computational intelligence principles and methods in their respective research communities. Professionals working on systems engineering, communications, innovative computing systems and adaptive technologies for sustainable growth, will also be able to benefit from the information provided in the book.
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48

(Editor), David Helmbold y Bob Williamson (Editor), eds. Computational Learning Theory: 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning ... (Lecture Notes in Computer Science). Springer, 2001.

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49

(Editor), Paul Fischer y Hans U. Simon (Editor), eds. Computational Learning Theory: 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings (Lecture Notes in Computer Science). Springer, 1999.

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50

Hennig, Philipp, Hans P. Kersting y Michael A. Osborne. Probabilistic Numerics: Computation As Machine Learning. Cambridge University Press, 2022.

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