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Relevant bibliographies by topics / Machine learning / Books

Books on the topic 'Machine learning'

To see the other types of publications on this topic, follow the link: Machine learning.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 July 2024

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1

Zhou, Zhi-Hua. Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.

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Jung, Alexander. Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.

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3

Mitchell, Tom M., Jaime G. Carbonell, and Ryszard S. Michalski. Machine Learning. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.

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4

Fernandes de Mello, Rodrigo, and Moacir Antonelli Ponti. Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.

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5

Bell, Jason. Machine Learning. Indianapolis, IN, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.

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6

Huang, Kaizhu, Haiqin Yang, Irwin King, and Michael Lyu. Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.

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7

Jebara, Tony. Machine Learning. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.

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8

Vorobeychik, Yevgeniy, and Murat Kantarcioglu. Adversarial Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01580-9.

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9

Chen, Zhiyuan, and Bing Liu. Lifelong Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01581-6.

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10

Tsihrintzis, George A., Dionisios N. Sotiropoulos, and Lakhmi C. Jain, eds. Machine Learning Paradigms. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-94030-4.

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11

Hutter, Frank, Lars Kotthoff, and Joaquin Vanschoren, eds. Automated Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5.

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12

Sun, Shiliang, Liang Mao, Ziang Dong, and Lidan Wu. Multiview Machine Learning. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3029-2.

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13

Zhang, Cha, and Yunqian Ma, eds. Ensemble Machine Learning. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9326-7.

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14

Tsihrintzis, George A., Maria Virvou, Evangelos Sakkopoulos, and Lakhmi C. Jain, eds. Machine Learning Paradigms. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15628-2.

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15

Carter, Eric, and Matthew Hurst. Agile Machine Learning. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5107-2.

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Vermeulen, Andreas François. Industrial Machine Learning. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5316-8.

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Amunategui, Manuel, and Mehdi Roopaei. Monetizing Machine Learning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3873-8.

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18

Forsyth, David. Applied Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7.

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19

Lampropoulos, Aristomenis S., and George A. Tsihrintzis. Machine Learning Paradigms. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19135-5.

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20

Golden, Richard M. Statistical Machine Learning. First edition. j Boca Raton, FL : CRC Press, 2020. j Includes bibliographical references and index.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351051507.

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21

Sotiropoulos, Dionisios N., and George A. Tsihrintzis. Machine Learning Paradigms. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47194-5.

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22

Paluszek, Michael, and Stephanie Thomas. MATLAB Machine Learning. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2250-8.

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23

Moshkov, Mikhail, and Beata Zielosko. Combinatorial Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20995-6.

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24

Tsihrintzis, George A., and Lakhmi C. Jain, eds. Machine Learning Paradigms. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49724-8.

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25

Virvou, Maria, Efthimios Alepis, George A. Tsihrintzis, and Lakhmi C. Jain, eds. Machine Learning Paradigms. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-13743-4.

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26

Choo, Kenny, Eliska Greplova, Mark H. Fischer, and Titus Neupert. Machine Learning kompakt. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-32268-7.

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27

Jo, Taeho. Machine Learning Foundations. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65900-4.

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28

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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Chen, Zhiyuan, and Bing Liu. Lifelong Machine Learning. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-031-01575-5.

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30

O'Shea, Tim. The learning machine. London: BBC Education, 1985.

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31

Cholewa, Valentin. Machine-Learning Basics for Beginners : Machine Learning Methods: Enterprise Machine Learning Guide. Independently Published, 2021.

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32

Liu, Shaowu, and Zhi-Hua Zhou. Machine Learning. Springer, 2020.

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33

Marsland, Stephen. Machine Learning. Chapman and Hall/CRC, 2014. http://dx.doi.org/10.1201/b17476.

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34

Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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Abstract:

This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described by a set of features. These techniques can be applied to learn knowledge required for a variety of problems in computational linguistics ranging from part-of-speech tagging and syntactic parsing to word-sense disambiguation and anaphora resolution. Finally, this article reviews the applications to a variety of these problems, such as morphology, part-of-speech tagging, word-sense disambiguation, syntactic parsing, semantic parsing, information extraction, and anaphora resolution.

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35

Zhang, Yagang, ed. Machine Learning. InTech, 2010. http://dx.doi.org/10.5772/217.

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36

Machine Learning. United States: University of California, 2018. http://dx.doi.org/10.4135/9781529795417.

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37

Machine Learning. Elsevier, 1990. http://dx.doi.org/10.1016/c2009-0-27578-7.

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38

Machine Learning. Elsevier, 1991. http://dx.doi.org/10.1016/c2009-0-27657-4.

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39

Machine Learning. Elsevier, 2015. http://dx.doi.org/10.1016/c2013-0-19102-7.

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40

Machine Learning. Elsevier, 2018. http://dx.doi.org/10.1016/c2015-0-00237-4.

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41

Machine Learning. Elsevier, 2020. http://dx.doi.org/10.1016/c2017-0-03724-2.

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Machine Learning. Elsevier, 2020. http://dx.doi.org/10.1016/c2019-0-03772-7.

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43

Kang, Minsoo, and Eunsoo Choi. Machine Learning. WORLD SCIENTIFIC, 2021. http://dx.doi.org/10.1142/12037.

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44

Marsland, Stephen. Machine Learning. Chapman and Hall/CRC, 2011. http://dx.doi.org/10.1201/9781420067194.

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45

Theodoridis, Sergios. Machine Learning. Elsevier Science & Technology, 2020.

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46

Howey, Hugh, and Zackman Scott Aiello Hugh Howey Gabra. Machine Learning. Audible Studios on Brilliance, 2018.

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47

Rossetti, Giulio, and Massimo Buscema. Machine Learning. Excelic Press LLC, 2018.

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48

Machine learning. Boston/U.S.A: Kluwer Academic Publishers, 1986.

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49

Theodoridis, Sergios. Machine Learning. Elsevier Science & Technology Books, 2020.

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50

Machine Learning. McGraw-Hill Science/Engineering/Math, 1997.

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