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Libros sobre el tema "Machine learning tools"

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Autor: Grafiati
Publicado: 6 de septiembre de 2023
Última modificación: 7 de septiembre de 2023

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1

Khosrowpour, Mehdi y Information Resources Management Association. Machine learning: Concepts, methodologies, tools and applications. Hershey, PA: Information Science Reference, 2012.

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2

Learning computer numerical control. Albany, NY: Delmar Publishers, 1992.

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3

Cost-sensitive machine learning. Boca Raton, FL: CRC Press, 2012.

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4

Eibe, Frank y Hall Mark A, eds. Data mining: Practical machine learning tools and techniques. 3a ed. Burlington, MA: Morgan Kaufmann, 2011.

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5

Castiello, Maria Elena. Computational and Machine Learning Tools for Archaeological Site Modeling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88567-0.

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6

Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012.

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7

Pardalos, Panos M., Stamatina Th Rassia y Arsenios Tsokas, eds. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84459-2.

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8

Witten, I. H. Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, Calif: Morgan Kaufmann, 2000.

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9

Srinivasa, K. G., G. M. Siddesh y S. R. Manisekhar, eds. Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5.

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10

National Institute of Standards and Technology (U.S.), ed. Manufacturing technology learning modules: Sharing resources for school outreach. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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11

Bernhard, Schölkopf, Burges Christopher J. C y Smola Alexander J, eds. Advances in kernel methods: Support vector learning. Cambridge, Mass: MIT Press, 1999.

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12

Statistical learning and data science. Boca Raton: CRC Press, 2012.

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13

Wright, Ivy. Machine Learning: Concepts, Tools and Techniques. States Academic Press, 2022.

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14

Irma. Machine Learning: Concepts, Methodologies, Tools and Applications. IGI Global, 2011.

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15

IRMA. Machine Learning: Concepts, Methodologies, Tools and Applications. Information Science Reference, 2011.

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16

Choi, Eunsoo y Minsoo Kang. Machine Learning: Concepts, Tools and Data Visualization. World Scientific Publishing Co Pte Ltd, 2021.

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17

IRMA. Machine Learning: Concepts, Methodologies, Tools and Applications. Information Science Reference, 2011.

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18

Choi, Eunsoo y Minsoo Kang. Machine Learning: Concepts, Tools and Data Visualization. World Scientific Publishing Co Pte Ltd, 2021.

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19

Janke, Michael. Learning Computer Numerical Control: Instructor's Guide. Natl Tooling & Machining Assn, 1996.

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20

Mather, Bob. Machine Learning in Python: Hands on Machine Learning with Python Tools, Concepts and Techniques. Independently Published, 2018.

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21

Yu, Shipeng, Balaji Krishnapuram y R. Bharat Rao. Cost-Sensitive Machine Learning. Taylor & Francis Group, 2019.

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22

Yu, Shipeng, Balaji Krishnapuram y R. Bharat Rao. Cost-Sensitive Machine Learning. Taylor & Francis Group, 2011.

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23

Yu, Shipeng, Balaji Krishnapuram y R. Bharat Rao. Cost-Sensitive Machine Learning. Taylor & Francis Group, 2011.

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24

Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, 2011. http://dx.doi.org/10.1016/c2009-0-19715-5.

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25

Data mining : practical machine learning tools and techniques. Morgan Kaufmann, 2017.

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26

Witten, Ian H., Eibe Frank, Hall Mark A y Christopher Pal. Data Mining: Practical Machine Learning Tools and Techniques. Elsevier Science & Technology Books, 2016.

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27

Data Mining: Practical Machine Learning Tools and Techniques. Elsevier Science & Technology Books, 2011.

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28

Eddaly, Mansour, Patrick Siarry y Bassem Jarboui. Metaheuristics for Machine Learning: New Advances and Tools. Springer, 2022.

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29

Castiello, Maria Elena. Computational and Machine Learning Tools for Archeological Site Modeling. Springer International Publishing AG, 2021.

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30

Computational and Machine Learning Tools for Archaeological Site Modeling. Springer International Publishing AG, 2023.

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31

Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.

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32

Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.

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33

Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications. Wiley & Sons, Limited, John, 2020.

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34

Dulhare, Uma N., Khaleel Ahmad y Khairol Amali Bin Ahmad. Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications. Wiley & Sons, Incorporated, John, 2020.

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35

Witten, Ian H. y Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Elsevier Science & Technology Books, 2005.

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36

Dulhare, Uma N., Khaleel Ahmad y Khairol Amali Bin Ahmad. Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications. Wiley & Sons, Incorporated, John, 2020.

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37

Farth, Thomas. Machine Learning: Your Ultimate Guide for Concepts, Tools and Techniques. Independently Published, 2018.

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38

Dulhare, Uma N., Khaleel Ahmad y Khairol Amali Bin Ahmad. Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications. Wiley & Sons, Limited, John, 2020.

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39

Basuchoudhary, Atin, James T. Bang y Tinni Sen. Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth. Springer, 2017.

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40

Etaati, Leila. Machine Learning with Microsoft Technologies: Selecting the Right Architecture and Tools for Your Project. Apress, 2019.

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41

Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Incorporated, 2022.

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42

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019.

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43

Modern Advances In Intelligent Systems And Tools. Springer, 2012.

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44

Nagel, Stefan. Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.001.0001.

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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. This book examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, the book discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. The book presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
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45

Nagler, Dylan J. SCHUBOT: Machine learning tools for the automated analysis of Schubert's Lieder. 2014.

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46

Shaikh, Tawseef Ayoub, Tabasum Rasool y Saqib Hakak. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques. Taylor & Francis Group, 2023.

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47

Shaikh, Tawseef Ayoub, Tabasum Rasool y Saqib Hakak. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques. Taylor & Francis Group, 2023.

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48

Shaikh, Tawseef Ayoub, Tabasum Rasool y Saqib Hakak. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques. Taylor & Francis Group, 2023.

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49

Witten, Ian H. y Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Elsevier Science & Technology Books, 1999.

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50

Fiebrink, Rebecca A. y Baptiste Caramiaux. The Machine Learning Algorithm as Creative Musical Tool. Editado por Roger T. Dean y Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.23.

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Machine learning is the capacity of a computational system to learn structure from data in order to make predictions on new data. This chapter draws on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. It motivates a new understanding of learning algorithms as human-computer interfaces: like other interfaces, learning algorithms can be characterized by the ways their affordances intersect with goals of human users. The chapter also argues that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates a concluding discussion of what it means to employ machine learning as a creative tool.
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