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Books on the topic 'Machine learning for information improvement'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Swain, Debabala, Prasant Kumar Pattnaik, and Pradeep K. Gupta, eds. Machine Learning and Information Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3.

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2

Swain, Debabala, Prasant Kumar Pattnaik, and Tushar Athawale, eds. Machine Learning and Information Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4859-2.

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3

Houser, David Allan. Machine learning as a quality improvement tool. National Library of Canada, 1996.

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4

MACKAY, DAVID J. C. Information Theory, Inference & Learning Algorithms. Cambridge University Press, 2003.

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5

Terry, Caelli, ed. A compendium of machine learning. Ablex Pub. Corp., 1996.

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6

Jebara, Tony. Machine Learning: Discriminative and Generative. Springer US, 2004.

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7

Machine learning of robot assembly plans. Kluwer Academic Publishers, 1988.

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8

1951-, Rada R., ed. Machine learning: Applications in expert systems and information retrieval. E. Horwood, 1986.

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9

Príncipe, J. C. Information theoretic learning: Renyi's entropy and kernel perspectives. Springer, 2010.

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10

Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2013.

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11

1945-, Basak Subhash C., ed. Statistical and machine learning approaches for network analysis. Wiley, 2012.

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12

Dacheng, Tao, ed. Modern machine learning techniques and their applications in cartoon animation research. IEEE Press/Wiley, 2013.

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13

Wei, Chia-Hung. Machine learning techniques for adaptive multimedia retrieval: Technologies, applications, and perspectives. Information Science Reference, 2011.

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14

1967-, Meira Wagner, ed. Demand-driven associative classification. Springer, 2011.

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15

Emde, Werner. Modellbildung, Wissensrevision und Wissensrepräsentation im Maschinellen Lernen. Springer, 1991.

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16

Learning to rank for information retrieval and natural language processing. Morgan & Claypool, 2011.

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17

S, Chen Peter P., Wong Leah Y, and International Conference on Conceptual Modeling (25th : 2006 : Tucson, Ariz.), eds. Active conceptual modeling of learning: Next generation learning-base system development. Springer, 2007.

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18

Daniele, Gunetti, ed. Inductive logic programming: From machine learning to software engineering. MIT Press, 1996.

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19

Kersting, Kristian. An inductive logic programming approach to statistical relational learning. IOS Press, 2006.

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20

Learning OWL class expressions. AKA Akademische Verlagesellschaft, 2010.

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21

Concurrent learning and information processing: A neuro-computing system that learns during monitoring, forecasting, and control. Chapman & Hall, 1997.

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22

Miske, Shirley J. Training and learning in Mangochi classrooms: Combining qualitative and quantitative information to study twelve primary schools in Malawi. s.n., 1998.

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23

Hassanien, Aboul Ella. Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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24

Jahrestagung, Gesellschaft für Klassifikation. Data analysis, machine learning and applications: Proceedings of the 31st Annual Conference of the Gesellschaft fü̈r Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007. Edited by Preisach Christine. Springer, 2008.

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25

Pacific, Rim International Conference on Artificial Intelligence (4th 1996 Cairns Qld ). PRICAI '96: Topics in artificial intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : proceedings. Springer, 1996.

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26

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelli

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27

Jebara, Tony. Machine Learning. Springer, 2011.

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28

Symmetry-Adapted Machine Learning for Information Security. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03936-643-9.

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29

Machine Learning: Applications in Expert Systems & Information Retrieval. Prentice Hall Europe (a Pearson Education company), 1986.

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30

Caelli, Terry, and Garry Briscoe. A Compendium of Machine Learning: Symbolic Machine Learning (Ablex Series in Artificial Intelligence). Ablex Publishing Corporation, 1995.

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31

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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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 b

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32

Machine learning for information extraction: Papers from the AAAI workshop. AAAI Press, 1999.

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33

Machine Learning And Knowledge Discovery In Databases. Springer, 2010.

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34

(Editor), Haym Hirsh, and Marti Hearst (Editor), eds. Machine Learning in Information Access: Papers from the 1996 Spring Symposium (Technical Report). AAAI Press, 1996.

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35

Stamp, Mark. Introduction to Machine Learning with Applications in Information Security. Taylor & Francis Group, 2017.

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36

Dua, Mohit, and Ankit Kumar Jain. Machine Learning Techniques for Pattern Recognition and Information Security. IGI Global, 2020.

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37

Stamp, Mark. Introduction to Machine Learning with Applications in Information Security. Taylor & Francis Group, 2017.

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38

Gupta, Pradeep K., Prasant Kumar Pattnaik, and Debabala Swain. Machine Learning and Information Processing: Proceedings of ICMLIP 2019. Springer, 2020.

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39

Dua, Mohit, and Ankit Kumar Jain. Machine Learning Techniques for Pattern Recognition and Information Security. IGI Global, 2020.

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40

Introduction to machine learning with applications in information security. CRC Press, Taylor & Francis Group, 2018.

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41

Dua, Mohit, and Ankit Kumar Jain. Machine Learning Techniques for Pattern Recognition and Information Security. IGI Global, 2020.

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42

Stamp, Mark. Introduction to Machine Learning with Applications in Information Security. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315213262.

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43

Dua, Mohit, and Ankit Kumar Jain. Machine Learning Techniques for Pattern Recognition and Information Security. IGI Global, 2020.

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44

Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 2008.

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45

Machine Learning for Spatial Environmental Data. Efpl Press, 2009.

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46

Unrules: Success in the Age of Robots and Machine-Learning. Wiley & Sons, Limited, John, 2018.

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47

Lendvai, Piroska kornelia. Extracting information from spoken user input' a machine learning approach. Brabantse Universiteiten, 2004.

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48

Forsyth, R., and R. Rada. Machine Learning (Ellis Horwood Series Artificial Intelligence). Ellis Horwood, 1986.

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49

Machine Learning And Knowledge Discovery In Databases. Springer, 2008.

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50

Katharina, Morik, ed. Knowledge representation and organization in machine learning. Springer-Verlag, 1989.

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