Optimization in Machine Learning and Applications (eBook)

Anand J. Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore; an M.S. in AI from the University of Regina, Canada; and Bachelor of Engineering from Shivaji University, India. He worked as a Research Fellow on a cross-border supply-chain disruption project at Odette School of Business, University of Windsor, Canada. Currently, he is the Head and an Associate Professor at the Symbiosis Institute of Technology, Pune, India. His research interests include optimization algorithms, multiobjective optimization, multiagent systems, complex systems, swarm optimization, game theory, and self-organizing systems. He is the founder and Chairman of the OAT Research Lab. Anand has published over 40 research papers in peer-reviewed journals and conferences as well as two books.

Suresh Chandra Satapathy is a Professor at the School of Computer Engineering, KIIT, Odisha, India. Previously, he was a Professor and the Head of the Department of CSE at ANITS, AP, India. He received his Ph.D. in CSE from JNTU, Hyderabad, and M.Tech. in CSE from the NIT, Odisha. He has more than 27 years of teaching and research experience. His research interests include machine learning, data mining, swarm intelligence and applications. He has published more than 98 papers in respected journals and conferences and has edited numerous volumes for Springer AISC and LNCS. In addition to serving on the editorial board of several journals, he is a senior member of the IEEE and a life member of the Computer Society of India, where he is the National Chairman of Division-V (Education and Research).

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This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.