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Books on the topic 'Learning algorithm'

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Published: 4 June 2021
Last updated: 25 May 2024

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1

Rao, R. Venkata. Teaching Learning Based Optimization Algorithm. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22732-0.

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2

Bhatia, Praveen. A learning algorithm for robotic assembly. Dearborn: Society of Manufacturing Engineers, 1989.

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3

Analog algorithm: Landscapes of machine learning. Salzburg: Fotohof Edition, 2020.

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4

Falkenhainer, Brian. The structure-mapping engine: Algorithm and examples. Urbana, Ill. (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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5

Lowton, Andrew David. A constructive learning algorithm based on back-propagation. Birmingham: Aston University. Department ofComputer Science and Applied Mathematics, 1995.

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6

Blume, Christian. GLEAM - general learning evolutionary algorithm and method: Ein evolutionärer Algorithmus und seine Anwendungen. Karlsruhe: KIT Scientific Publ., 2009.

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7

Köpf, Christian Rudolf. Meta-learning: Strategies, implementations, and evaluations for algorithm selection. Berlin: Aka, 2006.

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8

Mamduh Mustafa Awd, Mustafa. Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-40237-2.

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9

Valdez, Fevrier, Juan Barraza, and Patricia Melin. Hybrid Competitive Learning Method Using the Fireworks Algorithm and Artificial Neural Networks. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47712-6.

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10

Travis, Kerzic, and United States. National Aeronautics and Space Administration., eds. mGA1.0: A common LISP implementation of a messy genetic algorithm. [Houston, Tex.]: Research Institute for Computing and Information Systems, University of Houston, Clear Lake, 1990.

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11

Travis, Kerzic, and United States. National Aeronautics and Space Administration., eds. mGA1.0: A common LISP implementation of a messy genetic algorithm. [Houston, Tex.]: Research Institute for Computing and Information Systems, University of Houston, Clear Lake, 1990.

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12

Lima, Pedro U. Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems. Troy, N.Y: Center for Intelligent robotic systems for space exploration, 1992.

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13

Lima, Pedro. Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems. Troy, N.Y: Center for Intelligent robotic systems for space exploration, 1992.

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14

Hutchinson, Alan. Algorithmic learning. Oxford: Clarendon Press, 1994.

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15

The design and analysis of efficient learning algorithms. Cambridge, Mass: MIT Press, 1992.

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16

Celebi, M. Emre, and Kemal Aydin, eds. Unsupervised Learning Algorithms. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8.

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17

Li, Fuwei, Lifeng Lai, and Shuguang Cui. Machine Learning Algorithms. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16375-3.

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18

Kaelbling, Leslie Pack. Learning in embedded systems. Cambridge, Mass: MIT Press, 1993.

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19

Kaelbling, Leslie Pack. Learning in embedded systems. Stanford, Calif: Dept. of Computer Science, Stanford University, 1990.

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20

Gilles, Stoltz, Vayatis Nicolas, Zeugmann Thomas, and SpringerLink (Online service), eds. Algorithmic Learning Theory: 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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21

Zhang, Yi, and Zhou Jiliu, eds. Subspace learning of neural networks. Boca Raton: CRC Press, 2011.

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22

Szepesvári, Csaba. Algorithms for Reinforcement Learning. Cham: Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01551-9.

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23

Ayyadevara, V. Kishore. Pro Machine Learning Algorithms. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5.

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24

Szepesvári, Csaba. Algorithms for reinforcement learning. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.

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25

Schapire, Robert E. Boosting: Foundations and algorithms. Cambridge, MA: MIT Press, 2012.

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26

Watanabe, Osamu, and Takashi Yokomori, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46769-6.

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27

Bshouty, Nader H., Gilles Stoltz, Nicolas Vayatis, and Thomas Zeugmann, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34106-9.

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28

Jantke, Klaus P., Takeshi Shinohara, and Thomas Zeugmann, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60454-5.

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29

Jain, Sanjay, Rémi Munos, Frank Stephan, and Thomas Zeugmann, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40935-6.

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30

Arimura, Hiroki, Sanjay Jain, and Arun Sharma, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40992-0.

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31

Hutter, Marcus, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16108-7.

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32

Bulman, Alan. Bulman Learning Algorithm. Trafford Publishing, 2006.

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33

Malitsky, Yuri. Instance-Specific Algorithm Configuration. Springer, 2014.

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34

Malitsky, Yuri. Instance-Specific Algorithm Configuration. Springer, 2016.

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35

Malitsky, Yuri. Instance-Specific Algorithm Configuration. Springer, 2014.

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36

Joshi, Varad Vidyadhar. Expert-gate algorithm. 1992.

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37

Joshi, Varad Vidyadhar. Expert-gate algorithm. 1992.

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38

Fiebrink, Rebecca A., and Baptiste Caramiaux. The Machine Learning Algorithm as Creative Musical Tool. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.23.

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Machine learning is the capacity of a computational system to learn structure from data in order to make predictions on new data. This chapter draws on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. It motivates a new understanding of learning algorithms as human-computer interfaces: like other interfaces, learning algorithms can be characterized by the ways their affordances intersect with goals of human users. The chapter also argues that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates a concluding discussion of what it means to employ machine learning as a creative tool.
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39

Rao, R. Venkata Venkata. Teaching Learning Based Optimization Algorithm: And Its Engineering Applications. Springer, 2016.

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40

Becker, Helen Suzanna. An information-theoretic unsupervised learning algorithm for neural networks. 1993.

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41

Awd, Mustafa Mamduh Mustafa. Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing. Springer Fachmedien Wiesbaden GmbH, 2023.

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42

Rao, R. Venkata. Teaching Learning Based Optimization Algorithm: And Its Engineering Applications. Springer, 2015.

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43

Rao, R. Venkata. Teaching Learning Based Optimization Algorithm: And Its Engineering Applications. Springer, 2015.

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44

Club, Rocket Baby. Toby's Little Trees : Machine Learning For Kids: Nearest Neighbor Algorithm. Rocket Baby Club, 2018.

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45

Graham, S. Scott. The Doctor and the Algorithm. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780197644461.001.0001.

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Abstract The Doctor and the Algorithm combines insights from science and technology studies, critical algorithm studies, and public interest informatics to better understand how health artificial intelligence (AI) is made, promoted, regulated, and justified. It reviews the enthusiastic promises of health AI marketing communication and medical futurism as well as concerns about the dangerous and inequitable effects of AI that have been raised in critical algorithm studies and bioethics. In so doing, The Doctor and the Algorithm investigates promising new applications for health AI, as well as how these new technologies can replicate human biases under a veneer of objectivity and lead to considerable harms. The book also leverages AI, itself, to investigate the foundations of machine-learning technologies and the use of promotional language in health AI research. Along the way, it explores both how the overly enthusiastic embrace of health AI can lead to unintended consequences for patients and public health and how new initiatives in ethical and just AI can support better futures for algorithmic medicine. The Doctor and the Algorithm closes with recommendations for practices and frameworks that can support these more ethical and just approaches to health AI.
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46

Kumar, Sandeep, Rohit Raja, Shilpa Rani, and Shrikant Tiwari. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm. Wiley & Sons, Limited, John, 2022.

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47

Mazumder, Pinaki, and Nan Zheng. Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design. Wiley & Sons, Incorporated, John, 2019.

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48

Kumar, Sandeep, Rohit Raja, Shrikant Tiwari, and Shilpa Rani, eds. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm. Wiley, 2021. http://dx.doi.org/10.1002/9781119792109.

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49

Mazumder, Pinaki, and Nan Zheng. Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design. Wiley & Sons, Incorporated, John, 2019.

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50

Mazumder, Pinaki, and Nanchuan Zheng. Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design. Wiley & Sons, Limited, John, 2020.

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