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Books on the topic 'Stochastic optimization machine learning modeling'

Author: Grafiati

Published: 9 March 2023

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1

Poznyak, Alexander S. Learning automata and stochastic optimization. Springer, 1997.

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2

K, Najim, ed. Learning automata and stochastic optimization. Springer, 1997.

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3

Li, Fengpei. Stochastic Methods in Optimization and Machine Learning. [publisher not identified], 2021.

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4

Lan, Guanghui. First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1.

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5

S, Sastry P., ed. Networks of learning automata: Techniques for online stochastic optimization. Kluwer Academic, 2004.

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6

Thathachar, Mandayam A. L. Networks of learning automata: Techniques for online stochastic optimization. Kluwer Academic, 2003.

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7

Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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8

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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9

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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10

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. CRC Press LLC, 2021.

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11

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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12

Lan, Guanghui. First-Order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing AG, 2021.

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13

Lan, Guanghui. First-order and Stochastic Optimization Methods for Machine Learning. Springer, 2020.

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14

Thathachar, M. A. L., and P. S. Sastry. Networks of Learning Automata: Techniques for Online Stochastic Optimization. Springer, 2003.

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15

Thathachar, M. A. L. Networks of Learning Automata: Techniques For Online Stochastic Optimization. Springer, 2012.

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16

Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence Book 33). Springer, 2007.

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17

Sastry, Kumara, Martin Pelikan, and Erick Cantú-Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, 2010.

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18

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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19

(Editor), Martin Pelikan, Kumara Sastry (Editor), and Erick Cantú-Paz (Editor), eds. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence). Springer, 2006.

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20

Mehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh, and Sergio Márquez Sánchez, eds. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.

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The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.

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21

The Expected Knowledge: What can we know about anything and everything? Sivashanmugam Palaniappan, 2012.

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