Books on the topic 'DYNAMIC MACHINE LEARNING METHODOLOGY'

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

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

Bennaceur, Amel, Reiner Hähnle, and 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.

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.

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.

Kelly, Michael A. A methodology for software cost estimation using machine learning techniques. Monterey, Calif: Naval Postgraduate School, 1993.

Maximize the teaching/learning dynamic: A developmental approach for educators. 3rd ed. Denver, Colo: Higher Level, 2013.

Slater, Stanley F. Information search style and business performance in dynamic and stable environments: An exploratory study. Cambridge, Mass: Marketing Science Institute, 1997.

Ehramikar, Soheila. The enhancement of credit card fraud detection systems using machine learning methodology. Ottawa: National Library of Canada, 2000.

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.

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.

Building intelligent agents: An apprenticeship multistrategy learning theory, methodology, tool and case studies. San Diego: Academic Press, 1998.

Barbakh, Wesam Ashour. Non-standard parameter adaptation for exploratory data analysis. Berlin: Springer, 2009.

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

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

Rieser, 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.

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

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

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

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

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

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

Russell, David W. BOXES Methodology Second Edition: Black Box Control of Ill-Defined Systems. Springer International Publishing AG, 2022.

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

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

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

Zeng, Tao, Tao Huang, and 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.

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

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

Jennie, Si, ed. Handbook of learning and approximate dynamic programming. Hoboken, NJ: IEEE Press, 2004.

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

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

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

Engles, Robert. The Methodology of Applying Machine Learning: Papers from the AAAI Workshop. AAAI Press, 1998.

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

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

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

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

Reinforcement learning and approximate dynamic programming for feedback control. IEEE Press, 2012.

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

Sen, Shampa, Leonid Datta, and Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.

Sen, Shampa, Leonid Datta, and Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.

Sen, Shampa, Leonid Datta, and Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.

Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.

Sen, Shampa, Leonid Datta, and Sayak Mitra. Machine Learning and IoT: A Biological Perspective. Taylor & Francis Group, 2018.

Language and Chronology: Text Dating by Machine Learning. BRILL, 2019.

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

Building Intelligent Agents: An Apprenticeship, Multistrategy Learning Theory, Methodology, Tool and Case Studies. Elsevier Science & Technology Books, 1998.