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Books on the topic 'Unsupervied learning'

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Author: Grafiati

Published: 14 March 2023

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1

Kyan, Matthew, Paisarn Muneesawang, Kambiz Jarrah, and Ling Guan. Unsupervised Learning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118875568.

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2

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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3

Li, Xiangtao, and Ka-Chun Wong, eds. Natural Computing for Unsupervised Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98566-4.

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4

Unsupervised Representation Learning with Correlations. [New York, N.Y.?]: [publisher not identified], 2020.

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5

Leordeanu, Marius. Unsupervised Learning in Space and Time. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42128-1.

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6

Baruque, Bruno, and Emilio Corchado. Fusion Methods for Unsupervised Learning Ensembles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16205-3.

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7

Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unsupervised Machine Learning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557693.

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8

Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1637-8.

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9

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

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10

Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001.

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11

Bartlett, Marian Stewart. Face image analysis by unsupervised learning. Boston: Kluwer Academic Publishers, 2001.

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12

Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Berlin: Springer, 2010.

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13

Berry, Michael W., Azlinah Mohamed, and Bee Wah Yap, eds. Supervised and Unsupervised Learning for Data Science. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22475-2.

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14

H, Fisher Douglas, Pazzani Michael John 1958-, and Langley Pat, eds. Concept formation: Knowledge and experience in unsupervised learning. San Mateo, Calif: Morgan Kaufmann Publishers, 1991.

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15

Aldrich, Chris, and Lidia Auret. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5185-2.

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16

Kunz, Nicholas Hyeong. Unsupervised Learning for Submarket Modeling: A Proxy for Neighborhood Change. [New York, N.Y.?]: [publisher not identified], 2019.

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17

Aldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. London: Springer London, 2013.

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18

Whitehead, P. A. Design considerations for a hardware accelerator for Kohonen unsupervised learning in artificial neural networks. Manchester: UMIST, 1997.

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19

Azuaje, Francisco Javier. An unsupervised neural learning approach to retrieval strategies for case-based reasoning and decision support. [s.l: The Author], 1999.

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20

Unsupervised and Weakly-Supervised Learning of Localized Texture Patterns of Lung Diseases on Computed Tomography. [New York, N.Y.?]: [publisher not identified], 2019.

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21

Glennon, Jonathan Scott. Feature-based localization in sonar-equipped autonomous mobile robots through hough transform and unsupervised learning network. Monterey, Calif: Naval Postgraduate School, 1998.

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22

Workshop on Unsupervised Learning in Natural Language Processing (1999 University of Maryland). Unsupervised learning in natural language processing: Proceedings of the workshop, 21 June 1999, University of Maryland, College Park, Maryland, USA. New Brunswick, N.J: Association for Computational Linguistics, 1999.

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23

Hinton, Geoffrey, and Terrence J. Sejnowski, eds. Unsupervised Learning. The MIT Press, 1999. http://dx.doi.org/10.7551/mitpress/7011.001.0001.

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24

Celebi, M. Emre, and Kemal Aydin. Unsupervised Learning Algorithms. Springer, 2018.

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25

Celebi, M. Emre, and Kemal Aydin. Unsupervised Learning Algorithms. Springer London, Limited, 2016.

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26

Unsupervised Learning Algorithms. Springer, 2016.

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27

Unsupervised Learning with R. Packt Publishing, 2015.

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28

Muneesawang, Paisarn, Ling Guan, Matthew Kyan, and Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.

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29

Muneesawang, Paisarn, Ling Guan, Matthew Kyan, and Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.

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30

Li, Xiangtao, and Ka-Chun Wong. Natural Computing for Unsupervised Learning. Springer, 2018.

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31

Li, Xiangtao, and Ka-Chun Wong. Natural Computing for Unsupervised Learning. Springer, 2018.

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32

Unsupervised Learning of Visuomotor Associations. Berlin, Germany: Logos-Verlag Berlin, 2005.

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33

Jo, Taeho. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Springer International Publishing AG, 2022.

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34

Jo, Taeho. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Springer International Publishing AG, 2021.

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35

Lopez, César Perez. Machine Learning with Matlab. Unsupervised Learning Techniques: Classification. Lulu Press, Inc., 2020.

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36

Perez, C. Machine Learning Techniques: UNSUPERVISED LEARNING. EXAMPLES with MATLAB. Independently Published, 2019.

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37

Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Springer, 2001.

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38

Sejnowski, Terrence J., Tomaso A. Poggio, and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 2016.

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39

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2014.

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40

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2010.

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41

Sejnowski, Terrence J., and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 1999.

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42

Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.

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Abstract:

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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43

Colins, Michael. Machine Learning: An Introduction To Supervised & Unsupervised Learning Algorithms. Createspace Independent Publishing Platform, 2017.

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44

Yang, Yun. Temporal Data Mining Via Unsupervised Ensemble Learning. Elsevier, 2016.

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45

Temporal Data Mining Via Unsupervised Ensemble Learning. Elsevier, 2017. http://dx.doi.org/10.1016/c2016-0-01428-6.

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46

Perez, C. Segmentation with Matlab. Unsupervised Machine Learning Techniques. Independently Published, 2019.

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47

Berry, Michael W., Bee Wah Yap, and Azlinah Mohamed. Supervised and Unsupervised Learning for Data Science. Springer International Publishing AG, 2020.

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48

Temporal Data Mining Via Unsupervised Ensemble Learning. Elsevier Science & Technology Books, 2016.

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49

Berry, Michael W., Bee Wah Yap, and Azlinah Mohamed. Supervised and Unsupervised Learning for Data Science. Springer, 2019.

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50

Unsupervised and Transfer Learning: Challenges in Machine Learning, Volume 7. Microtome Publishing, 2013.

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