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Books on the topic 'Machine learnings'

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Author: Grafiati
Published: 1 June 2024

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1

Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.

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2

Campbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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3

Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.

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4

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

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5

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

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6

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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7

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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8

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

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9

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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10

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

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11

Phillips, Charlene. The sewing machine classroom: Learning the ins and outs of your machine. Cincinnati, OH: Krause Publications, 2011.

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12

Lopes, Noel, and Bernardete Ribeiro. Machine Learning for Adaptive Many-Core Machines - A Practical Approach. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06938-8.

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13

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

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14

Steinwart, Ingo. Support vector machines. New York: Springer, 2008.

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15

Joachim, Diederich, ed. Rule extraction from support vector machines. Berlin: Springer, 2008.

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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

Stohr, Daniel Christoph. Beruflichen Anforderungen der Digitalisierung Hinsichtlich Formaler, Physischer und Kompetenzspezifischer Aspekte: Eine Analyse Von Stellenanzeigen Mittels Methoden des Text Minings und Machine Learnings. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2019.

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26

Stohr, Daniel Christoph. Die Beruflichen Anforderungen der Digitalisierung Hinsichtlich Formaler, Physischer und Kompetenzspezifischer Aspekte: Eine Analyse Von Stellenanzeigen Mittels Methoden des Text Minings und Machine Learnings. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2019.

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27

Stohr, Daniel Christoph. Die Beruflichen Anforderungen der Digitalisierung Hinsichtlich Formaler, Physischer und Kompetenzspezifischer Aspekte: Eine Analyse Von Stellenanzeigen Mittels Methoden des Text Minings und Machine Learnings. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2019.

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28

Stohr, Daniel Christoph. Die Beruflichen Anforderungen der Digitalisierung Hinsichtlich Formaler, Physischer und Kompetenzspezifischer Aspekte: Eine Analyse Von Stellenanzeigen Mittels Methoden des Text Minings und Machine Learnings. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2019.

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29

Goldberg, Andrew, and Xiaojin Zhu. Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers, 2009.

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30

Goldberg, Andrew, and Xiaojin Zhu. Introduction to Semi-supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning). Morgan & Claypool Publishers, 2008.

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31

Takano, Shigeyuki. Thinking Machines: Machine Learning and Its Hardware Implementation. Elsevier Science & Technology Books, 2021.

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32

Lopes, Noel, and Bernardete Ribeiro. Machine Learning for Adaptive Many-Core Machines - A Practical Approach. Springer, 2016.

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33

Lopes, Noel, and Bernardete Ribeiro. Machine Learning for Adaptive Many-Core Machines - A Practical Approach. Springer, 2014.

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34

Lopes, Noel, and Bernardete Ribeiro. Machine Learning for Adaptive Many-Core Machines - A Practical Approach. Springer, 2014.

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35

Lopes, Noel, and Bernardete Ribeiro. Machine Learning for Adaptive Many-Core Machines - a Practical Approach. Springer, 2014.

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36

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

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37

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

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38

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

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39

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.
40

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

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41

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

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42

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

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43

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

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44

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

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45

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

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46

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

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47

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

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48

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

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49

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

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50

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

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