Libros sobre el tema "DYNAMIC MACHINE LEARNING METHODOLOGY"
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Russell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Buscar texto completoGultekin, San. Dynamic Machine Learning with Least Square Objectives. [New York, N.Y.?]: [publisher not identified], 2019.
Buscar texto completoBennaceur, Amel, Reiner Hähnle y 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.
Texto completoHinders, 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.
Texto completoIEEE, 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.
Buscar texto completoKelly, Michael A. A methodology for software cost estimation using machine learning techniques. Monterey, Calif: Naval Postgraduate School, 1993.
Buscar texto completoMaximize the teaching/learning dynamic: A developmental approach for educators. 3a ed. Denver, Colo: Higher Level, 2013.
Buscar texto completoSlater, Stanley F. Information search style and business performance in dynamic and stable environments: An exploratory study. Cambridge, Mass: Marketing Science Institute, 1997.
Buscar texto completoEhramikar, Soheila. The enhancement of credit card fraud detection systems using machine learning methodology. Ottawa: National Library of Canada, 2000.
Buscar texto completoIEEE 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.
Buscar texto completoIEEE 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.
Buscar texto completoBuilding intelligent agents: An apprenticeship multistrategy learning theory, methodology, tool and case studies. San Diego: Academic Press, 1998.
Buscar texto completoBarbakh, Wesam Ashour. Non-standard parameter adaptation for exploratory data analysis. Berlin: Springer, 2009.
Buscar texto completoTrevor, Hastie, Tibshirani Robert y SpringerLink (Online service), eds. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer-Verlag New York, 2009.
Buscar texto completoAchmad, Widodo, ed. Introduction of intelligent machine fault diagnosis and prognosis. New York: Nova Science Publishers, 2009.
Buscar texto completoRieser, Verena. Reinforcement Learning for Adaptive Dialogue Systems: A Data-driven Methodology for Dialogue Management and Natural Language Generation. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.
Buscar texto completoHayes-Roth, Barbara. An architecture for adaptive intelligent systems. Stanford, Calif: Stanford University, Dept. of Computer Science, 1993.
Buscar texto completoRussell, David W. The BOXES Methodology: Black Box Dynamic Control. Springer, 2014.
Buscar texto completoThe BOXES Methodology: Black Box Dynamic Control. Springer, 2012.
Buscar texto completoLi, Fanzhang, Li Zhang y Zhao Zhang. Dynamic Fuzzy Machine Learning. de Gruyter GmbH, Walter, 2017.
Buscar texto completoLi, Fanzhang, Li Zhang y Zhao Zhang. Dynamic Fuzzy Machine Learning. de Gruyter GmbH, Walter, 2017.
Buscar texto completoLi, Fanzhang, Li Zhang y Zhao Zhang. Dynamic Fuzzy Machine Learning. de Gruyter GmbH, Walter, 2017.
Buscar texto completoRussell, David W. BOXES Methodology Second Edition: Black Box Control of Ill-Defined Systems. Springer International Publishing AG, 2022.
Buscar texto completoMuneesawang, Paisarn, Ling Guan, Matthew Kyan y Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.
Buscar texto completoMuneesawang, Paisarn, Ling Guan, Matthew Kyan y Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.
Buscar texto completoMuneesawang, Paisarn, Ling Guan, Matthew Kyan y Kambiz Jarrah. Unervised Learning Via Self-Organization: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.
Buscar texto completoZeng, Tao, Tao Huang y 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.
Texto completoJ, Walsh Thomas, Jonathan P. How, Alborz Geramifard, Stefanie Tellex y Girish Chowdhary. Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning. Now Publishers, 2013.
Buscar texto completoPowell, Warren B., Andrew G. Barto, Don Wunsch y Jennie Si. Handbook of Learning and Approximate Dynamic Programming. Wiley & Sons, Incorporated, John, 2012.
Buscar texto completoJennie, Si, ed. Handbook of learning and approximate dynamic programming. Hoboken, NJ: IEEE Press, 2004.
Buscar texto completoHinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer International Publishing AG, 2020.
Buscar texto completoHinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer International Publishing AG, 2021.
Buscar texto completoMachine Learning for Dynamic Software Analysis : Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, ... Papers. Springer, 2018.
Buscar texto completoEngles, Robert. The Methodology of Applying Machine Learning: Papers from the AAAI Workshop. AAAI Press, 1998.
Buscar texto completoHeo, Wookjae. Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques. Springer International Publishing AG, 2020.
Buscar texto completoHeo, Wookjae. Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques. Springer International Publishing AG, 2019.
Buscar texto completoLewis, Frank L. y Derong Liu. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Wiley & Sons, Incorporated, John, 2013.
Buscar texto completoLewis, Frank L. y Derong Liu. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Wiley & Sons, Incorporated, John, 2013.
Buscar texto completoReinforcement learning and approximate dynamic programming for feedback control. IEEE Press, 2012.
Buscar texto completoLewis, Frank L. y Derong Liu. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Wiley & Sons, Incorporated, John, 2013.
Buscar texto completoSen, Shampa, Leonid Datta y Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.
Buscar texto completoSen, Shampa, Leonid Datta y Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.
Buscar texto completoSen, Shampa, Leonid Datta y Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.
Buscar texto completoMachine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.
Buscar texto completoSen, Shampa, Leonid Datta y Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.
Buscar texto completoLanguage and Chronology: Text Dating by Machine Learning. BRILL, 2019.
Buscar texto completoRauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.
Buscar texto completoRauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.
Buscar texto completoRauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.
Buscar texto completoBuilding Intelligent Agents: An Apprenticeship, Multistrategy Learning Theory, Methodology, Tool and Case Studies. Elsevier Science & Technology Books, 1998.
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