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Livros sobre o tema "Dynamic machine learning"

Autor: Grafiati

Publicado: 22 de fevereiro de 2025

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1

Gultekin, San. Dynamic Machine Learning with Least Square Objectives. [New York, N.Y.?]: [publisher not identified], 2019.

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2

Bennaceur, Amel, Reiner Hähnle e Karl Meinke, eds. Machine Learning for Dynamic Software Analysis: Potentials and Limits. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96562-8.

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3

IEEE, International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.

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4

Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49395-0.

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5

IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.

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6

IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.

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7

Achmad, Widodo, ed. Introduction of intelligent machine fault diagnosis and prognosis. New York: Nova Science Publishers, 2009.

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8

Russell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.

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9

Hayes-Roth, Barbara. An architecture for adaptive intelligent systems. Stanford, Calif: Stanford University, Dept. of Computer Science, 1993.

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10

Duriez, Thomas, Steven L. Brunton e Bernd R. Noack. Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-40624-4.

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11

Chiroma, Haruna, Shafi’i M. Abdulhamid, Philippe Fournier-Viger e Nuno M. Garcia, eds. Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66288-2.

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12

Leigh, J. R. Control Theory. 2^a ed. Stevenage: IET, 2004.

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13

Li, Fanzhang, Li Zhang e Zhao Zhang. Dynamic Fuzzy Machine Learning. de Gruyter GmbH, Walter, 2017.

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14

Li, Fanzhang, Li Zhang e Zhao Zhang. Dynamic Fuzzy Machine Learning. de Gruyter GmbH, Walter, 2017.

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15

Li, Fanzhang, Li Zhang e Zhao Zhang. Dynamic Fuzzy Machine Learning. de Gruyter GmbH, Walter, 2017.

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16

Muneesawang, Paisarn, Ling Guan, Matthew Kyan e Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.

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17

Muneesawang, Paisarn, Ling Guan, Matthew Kyan e Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.

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18

J, Walsh Thomas, Jonathan P. How, Alborz Geramifard, Stefanie Tellex e Girish Chowdhary. Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning. Now Publishers, 2013.

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19

Muneesawang, Paisarn, Ling Guan, Matthew Kyan e Kambiz Jarrah. Unervised Learning Via Self-Organization: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.

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20

Learning from Data Streams in Dynamic Environments. Springer, 2015.

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21

Machine Learning for Dynamic Software Analysis : Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, ... Papers. Springer, 2018.

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22

Zeng, Tao, Tao Huang e Chuan Lu, eds. Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-554-2.

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23

Powell, Warren B., Andrew G. Barto, Don Wunsch e Jennie Si. Handbook of Learning and Approximate Dynamic Programming. Wiley & Sons, Incorporated, John, 2012.

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24

Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer International Publishing AG, 2021.

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25

Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer International Publishing AG, 2020.

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26

Lewis, Frank L., e Derong Liu. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Wiley & Sons, Incorporated, John, 2013.

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27

Lewis, Frank L., e Derong Liu. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Wiley & Sons, Incorporated, John, 2013.

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28

Lewis, Frank L., e Derong Liu. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Wiley & Sons, Incorporated, John, 2013.

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29

Heo, Wookjae. Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques. Springer International Publishing AG, 2020.

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30

Heo, Wookjae. Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques. Springer International Publishing AG, 2019.

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31

R Machine Learning by Example: Understand the Fundamentals of Machine Learning with R and Build Your Own Dynamic Algorithms to Tackle Complicated Real-World Problems Successfully. de Gruyter GmbH, Walter, 2016.

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32

Russell, David W. The BOXES Methodology: Black Box Dynamic Control. Springer, 2014.

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33

The BOXES Methodology: Black Box Dynamic Control. Springer, 2012.

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34

Pickreign, Cynthia J. Riggle: A program for the dynamic conceptual time series analysis of hypervariate data and its application to ecotoxicology. 1995.

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35

Amunategui, Manuel. Python Web Work - Online Presence Powerhouse: Grow Audiences, Use Html5 Templates, Serve Dynamic Content, Build Machine Learning Web Apps, Conquer the World. Independently Published, 2020.

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36

Salin, Sandra, e Cathy Hampton, eds. Innovative language teaching and learning at university: facilitating transition from and to higher education. Research-publishing.net, 2022. http://dx.doi.org/10.14705/rpnet.2022.56.9782490057986.

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Modern languages have always been about transition – as practitioners, we challenge our students constantly to move between their own cultural and linguistic reference points and those of others. Our dynamic, interactive teaching methodologies have had to adapt to the pandemic context, necessitating the interrogation of past practice and transition to new approaches. This volume presents case studies showcasing practical initiatives to promote creative, dialogic learning in the fluid contexts that modern foreign language students are currently experiencing as they transition to higher education post-Covid and to residence abroad post-Brexit, between online and face-to-face learning spaces and between machine- and person-centred learning.

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37

Noack, Bernd R., Steven L. Brunton e Thomas Duriez. Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Springer, 2018.

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38

Noack, Bernd R., Steven L. Brunton e Thomas Duriez. Machine Learning Control - Taming Nonlinear Dynamics and Turbulence. Springer London, Limited, 2016.

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39

Noack, Bernd R., Steven L. Brunton e Thomas Duriez. Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Springer, 2016.

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40

Decherchi, Sergio, Andrea Cavalli, Pratyush Tiwary e Francesca Grisoni, eds. Molecular Dynamics and Machine Learning in Drug Discovery. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-863-2.

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41

Iordache, Octavian. Self-Evolvable Systems: Machine Learning in Social Media. Springer Berlin / Heidelberg, 2014.

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42

Iordache, Octavian. Self-Evolvable Systems: Machine Learning in Social Media. Springer, 2012.

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43

Hanson, Stephen José, Michael J. Kearns, Thomas Petsche e Ronald L. Rivest, eds. Computational Learning Theory and Natural Learning Systems, Volume 2. The MIT Press, 1994. http://dx.doi.org/10.7551/mitpress/2029.001.0001.

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Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them. The first section provides theoretical explanations for the learning systems addressed, the second section focuses on issues in model selection and inductive bias, the third section presents new learning algorithms, the fourth section explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of learning algorithms. Bradford Books imprint

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44

Bansal, Vinnie, e Aurelien Clere. Machine Learning with Dynamics 365 and Power Platform: The Ultimate Guide to Learning and Applying Machine Learning and Predictive Analytics. Wiley & Sons, Limited, John, 2022.

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45

Ultimate Machine Learning Data Science: Statistical Methods for Building Trading Strategies to Machine Learning, Dynamical Systems, and Control for Beginners. Independently Published, 2022.

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46

Kutz, J. Nathan, e Steven L. Brunton. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019.

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47

Kutz, J. Nathan, e Steven L. Brunton. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2022.

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48

Kutz, J. Nathan, e Steven L. Brunton. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019.

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49

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2022.

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50

Mitra, Bivas, Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi e Jean-Charles Delvenne. Dynamics On and Of Complex Networks III: Machine Learning and Statistical Physics Approaches. Springer, 2019.

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