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Books on the topic 'Supervised Machine Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Cerulli, Giovanni. Fundamentals of Supervised Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41337-7.

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2

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

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3

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

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4

Vendan, S. Arungalai, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, and Akhil Garg. Welding and Cutting Case Studies with Supervised Machine Learning. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9382-2.

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5

Wuest, Thorsten. Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17611-6.

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6

SFI/CNLS Workshop on Formal Approaches to Supervised Learning (1992 Santa Fe, N.M.). The mathematics of generalization: The proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning. Edited by Wolpert David H. Addison-Wesley Pub. Co., 1995.

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7

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012.

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8

Robert, Tibshirani, and Friedman J. H, eds. The elements of statistical learning: Data mining, inference, and prediction : with 200 full-color illustrations. Springer, 2001.

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9

Trevor, Hastie, Tibshirani Robert, and SpringerLink (Online service), eds. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag New York, 2009.

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10

Hastie, Trevor. The elements of statistical learning: Data mining, inference, and prediction. Springer, 2001.

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11

Kolosova, Tanya, and Samuel Berestizhevsky. Supervised Machine Learning. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429297595.

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12

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

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13

Dhoot, Dr. Supervised Machine Learning for Kids. Tinker Toddlers, 2020.

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14

Dhoot, Dr. Supervised Machine Learning for Kids. Tinker Toddlers, 2020.

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15

King, Irwin, and Zenglin Xu. Introduction to Semi-Supervised Learning. Taylor & Francis Group, 2021.

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16

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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17

Marks, F. MACHINE LEARNING. SUPERVISED LEARNING TECHNIQUES Through R. Independently Published, 2020.

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18

Vidales, A. MACHINE LEARNING with MATLAB: NONPARAMETRIC SUPERVISED LEARNING. Independently Published, 2019.

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19

Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning. Springer, 2003.

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20

Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unervised Machine Learning. Wiley & Sons, Incorporated, John, 2013.

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21

Smith, Taylor. Supervised Machine Learning with Python: Develop Rich Python Coding Practices While Exploring Supervised Machine Learning. Packt Publishing, Limited, 2019.

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22

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

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23

Perez, C. MACHINE LEARNING with MATLAB. SUPERVISED LEARNING and REGRESSION. Independently Published, 2019.

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24

(Editor), Olivier Chapelle, Bernhard Schölkopf (Editor), and Alexander Zien (Editor), eds. Semi-Supervised Learning (Adaptive Computation and Machine Learning). The MIT Press, 2006.

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25

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

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26

Zhong, Guoqiang, and Kaizhu Huang. Semi-Supervised Learning: Background, Applications and Future Directions. Nova Science Publishers, Incorporated, 2018.

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27

Support vector machines applications. Springer, 2014.

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28

Ma, Yunqian, and Guodong Guo. Support Vector Machines Applications. Springer, 2016.

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29

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

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30

Partially Supervised Learning. Springer-Verlag Berlin and Heidelberg GmbH &, 2012.

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31

Bach, Francis, Yoav Freund, and Robert E. Schapire. Boosting: Foundations and Algorithms. MIT Press, 2012.

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32

Bach, Francis, Yoav Freund, and Robert E. Schapire. Boosting: Foundations and Algorithms. MIT Press, 2014.

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33

Freund, Yoav, and Robert E. Schapire. Boosting: Foundations and Algorithms. MIT Press, 2018.

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34

Hvitfeldt, Emil, and Julia Sigle. Supervised Machine Learning for Text Analysis in R. Taylor & Francis Group, 2021.

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35

Silge, Julia, and Emil Hvitfeldt. Supervised Machine Learning for Text Analysis in R. Taylor & Francis Group, 2021.

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36

Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unervised Machine Learning: Novel Strategies. Wiley & Sons, Incorporated, John, 2013.

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37

Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unervised Machine Learning: Novel Strategies. Wiley & Sons, Incorporated, John, 2011.

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38

Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unervised Machine Learning: Novel Strategies. Wiley & Sons, Incorporated, John, 2013.

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39

Silge, Julia, and Emil Hvitfeldt. Supervised Machine Learning for Text Analysis in R. Taylor & Francis Group, 2021.

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40

Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unervised Machine Learning: Novel Strategies. Wiley & Sons, Incorporated, John, 2013.

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41

Hvitfeldt, Emil, and Julia Sigle. Supervised Machine Learning for Text Analysis in R. Taylor & Francis Group, 2021.

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42

Silge, Julia, and Emil Hvitfeldt. Supervised Machine Learning for Text Analysis in R. Taylor & Francis Group, 2021.

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43

Perez, C. Machine Learning Techniques: SUPERVISED LEARNING and CLASSIFICATION. EXAMPLES with MATLAB. Independently Published, 2019.

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44

Sage, Anderson. Machine Learning Foundation: An Introduction to Supervised and Unsupervised Learning. Independently Published, 2022.

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45

RAFTER, H. SUPERVISED LEARNING TECHNIQUES in MACHINE LEARNING: CLASSIFICATION. Examples with SAS. Independently Published, 2020.

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46

Lorentz, C. MACHINE LEARNING with NEURAL NETWORKS: SUPERVISED LEARNING. EXAMPLES with MATLAB. Independently Published, 2020.

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47

Vidales, A. Machine Learning with Matlab: Supervised Learning Using Predictive Models. Regression. Independently Published, 2019.

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48

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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49

Niu, Xiaodong, S. Arungalai Vendan, Rajeev Kamal, Abhinav Karan, and Liang Gao. Welding and Cutting Case Studies with Supervised Machine Learning. Springer Singapore Pte. Limited, 2021.

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50

Vendan, S. Arungalai, Rajeev Kamal, and Abhinav Karan. Welding and Cutting Case Studies with Supervised Machine Learning. Springer, 2020.

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