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Books on the topic 'Perceptron'

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Published: 4 June 2021

Last updated: 14 March 2023

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1

Zheng, Gonghui. Design and evaluation of a multi-output-layer perceptron. [s.l: The Author], 1996.

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2

Ma, Zhe. A heuristic for general rule extraction from a multilayer perceptron. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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3

Peeling, S. M. Experiments in isolated digit recognition using the multi-layer perceptron. [London: HMSO, 1987.

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4

Lont, Jerzy B. Analog CMOS implementatrion of a multi-layer perceptron with nonlinear synapses. Kontanz: Hartung-Gorre, 1994.

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5

Harrison, R. F. The multi-layer perceptron as an aid to the early diagnosis of myocardial infarction. Sheffield: University of Sheffield, Dept. of Control Engineering, 1990.

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6

Harrison, R. F. Neur al networks,heart attack and bayesian decisions: An application oof the Boltzmann perceptron network. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1994.

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7

Ma, Zhe. Dynamic query algorithms for human-computer interaction based on information gain and the multi-layer perceptron. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1996.

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8

André, Delorme, and Flückiger Michelangelo 1939-, eds. Perception et réalité: Une introduction à la psychologie des perceptions. Boucherville, Qué: G. Morin, 2003.

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9

Beliveau, Marc. Canadian media's perceptions of Asia: Asian media's perception of Canada. Edited by Payrastre Georges, Phillips Susan 1950-, and Asia Pacific Foundation of Canada. [Canada]: Asia Pacific Foundation of Canada, 1992.

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10

Berthon, Pierre. Managers' perceptions of their decision-making context: The influence of perception type. Henley: The Management College, 1994.

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11

Sekuler, Robert. Perception. New York: A.A. Knopf, 1985.

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12

Troscianko, Tom, and Alastair Smith. Perception. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2010. http://dx.doi.org/10.4135/9781446262825.

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13

Sekuler, Robert. Perception. 2nd ed. New York: McGraw, 1990.

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14

Pautz, Adam. Perception. Abingdon, Oxon; New York, NY: Routledge, 2021. | Series: New problems of philosophy: Routledge, 2021. http://dx.doi.org/10.4324/9781315771939.

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15

Robert, Sekuler, ed. Perception. 5th ed. Boston: McGraw-Hill, 2006.

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16

Perception. Chesham, Bucks: Acumen, 2003.

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17

Heider, Fritz. Perception. München-Weinheim: Psychologie Verlags Union, 1988.

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18

Perception. London: Routledge, 1994.

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19

Randolph, Blake, ed. Perception. 4th ed. Boston: McGraw-Hill, 2002.

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20

Perception. Los Angeles: SAGE, 2010.

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21

Sekuler, Robert. Perception. 3rd ed. New York: McGraw-Hill, 1994.

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22

Kathleen, Akins, ed. Perception. New York: Oxford University Press, 1996.

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23

1917-, Carlsen G. Robert, and Carlsen Ruth Christoffer, eds. Perception. New York: Webster Division, McGraw-Hill, 1985.

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24

Sekuler, Robert. Perception. 2nd ed. New York: McGraw-Hill, 1990.

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25

Golija, Gaja. Tobias Putrih: Perceptron. Hatje Cantz Verlag GmbH & Co KG, 2023.

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26

Sonnet, Daniel. Neuronale Netze kompakt: Vom Perceptron zum Deep Learning. Springer Vieweg, 2021.

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27

Venema, Rienk S. How the perceptron reacts on non-separable classification problems. 1994.

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28

Dissertation: Autonomous Construction of Multi Layer Perceptron Neural Networks. Storming Media, 1997.

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29

Club, Rocket Baby. Toby's Video Game Puzzle : Machine Learning for Kids: Perceptron. Primedia eLaunch LLC, 2019.

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30

Ltd, ICON Group, and ICON Group International Inc. PERCEPTRON, INC.: Labor Productivity Benchmarks and International Gap Analysis (Labor Productivity Series). 2nd ed. Icon Group International, 2000.

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31

Ltd, ICON Group. PERCEPTRON, INC.: International Competitive Benchmarks and Financial Gap Analysis (Financial Performance Series). 2nd ed. Icon Group International, 2000.

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32

Mayor, Julien, Pablo Gomez, Franklin Chang, and Gary Lupyan, eds. 50 years after the perceptron, 25 years after PDP: Neural computation in language sciences. Frontiers Media SA, 2014. http://dx.doi.org/10.3389/978-2-88919-257-1.

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33

Nicholas-Bear. Fourth Perception: Quarta Perceptio Aeterna. Tellwell Talent, 2020.

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34

Nicholas-Bear, Fred. Fourth Perception: Quarta Perceptio Aeterna. Tellwell Talent, 2020.

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35

Nicholas-Bear. Third Perception: Tertia Perceptio Cum Papilionibus. Tellwell Talent, 2019.

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36

Nicholas-Bear. Third Perception: Tertia Perceptio cum Papilionibus. Tellwell Talent, 2019.

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37

Risk is a construct: Perceptions of risk perception. Münich: Knesebeck, 1993.

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38

Relationship between daughters' perceptions of their mothers and mothers' self-perception. 1989.

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39

Kenyon, George N., and Kabir C. Sen. Perception of Quality: Mapping Product and Service Quality to Consumer Perceptions. Springer London, Limited, 2016.

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40

Kenyon, George N., and Kabir C. Sen. Perception of Quality: Mapping Product and Service Quality to Consumer Perceptions. Springer, 2014.

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41

Kenyon, George N., and Kabir C. Sen. Perception of Quality: Mapping Product and Service Quality to Consumer Perceptions. Springer, 2014.

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42

Anderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0012.

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What form would a brain theory take? Would it be short and punchy, like Maxwell’s Equations? Or with a clear goal but achieved by a community of mechanisms—local theories—to attain that goal, like the US Tax Code. The best developed recent brain-like model is the “neural network.” In the late 1950s Rosenblatt’s Perceptron and many variants proposed a brain-inspired associative network. Problems with the first generation of neural networks—limited capacity, opaque learning, and inaccuracy—have been largely overcome. In 2016, a program from Google, AlphaGo, based on a neural net using deep learning, defeated the world’s best Go player. The climax of this chapter is a fictional example starring Sherlock Holmes demonstrating that complex associative computation in practice has less in common with accurate pattern recognition and more with abstract high-level conceptual inference.

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43

Millikan, Ruth Garrett. Perception, Especially Perception through Language. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198717195.003.0014.

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Perceptual processing is translation of patterns in the data of sense into cognitive understanding without uniceptual inference. Understanding language differs from ordinary perceptual processing in that the signs it translates are detached rather than attached. This similarity is obscured because ordinary uses of the verbs of perception do not track a kind of psychological processing. Their use is mostly factive, which encourages overlooking the fallibility of perception. One result is the mistaken view that perceptual illusions are an anomaly and that perception is cognitively impenetrable. The assumption that each of the senses has its own proprietary level of perception and the assumption that differences in the result of perceptual processing are always accompanied by differences in perceptual experience are questioned. Finally, a number of intuitive objections to the idea that understanding language is a form of perceptual processing are discussed.

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44

Pautz, Adam. Perception. Taylor & Francis Group, 2021.

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45

Maund, Barry. Perception. McGill-Queen's University Press, 2003.

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46

Bruno, Nicola, and Francesco Pavani. Perception. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198725022.001.0001.

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Perception: A Multisensory Perspective examines multisensory interactions as the key process behind how we perceive our own body, control its movements, perceive and recognize objects, respond to edible objects, perceive space, and perceive time. In addition, the book discusses multisensory processing in synaesthesia, multisensory attention, and the role of multisensory processing in learning. Multisensory phenomena in these domains are used to identify general principles, to introduce formal models, to present experimental methods, to discuss pathologies, and to illustrate applications within the domain of multisensory processing. The book is written to be understandable to the educated non-specialist and will be of interest to professionals who need to take into account multisensory processing in domains such as, for instance, physiotherapy and neurological rehabilitation, human–computer interfaces, or marketing. As the chapters address topics that are mostly left out of standard perception or medical textbooks, this book will also serve as a useful reference for the specialist perception scientist and for clinicians specialized in the treatment of sensory deficits.

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47

Leibo, Joel Z., and Tomaso Poggio. Perception. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0025.

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This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.

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48

Robinson, Howard. Perception. Routledge, 2003. http://dx.doi.org/10.4324/9780203164358.

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49

Maund, Barry. Perception. Routledge, 2014. http://dx.doi.org/10.4324/9781315710631.

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50

Wilding, John M. Perception. Routledge, 2017. http://dx.doi.org/10.4324/9781315464459.

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