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The LION Way: Machine Learning plus Intelligent Optimization, Version 3.0
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  • Title The LION Way: Machine Learning plus Intelligent Optimization, Version 3.0
  • Author(s) Roberto Battiti and Mauro Brunato
  • Publisher: CreateSpace, 1 edition (2014); eBook (University of Trento, Version 3.0 — December 2017)
  • Paperback: 366 pages
  • eBook: PDF (516 pages)
  • Language: English
  • ISBN-10: 1496034023
  • ISBN-13: 978-1496034021
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Book Description

Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex and dynamic problems. The LION way is about increasing the automation level and connecting data directly to decisions and actions. More power is directly in the hands of decision makers in a self-service manner, without resorting to intermediate layers of data scientists.

LION is a complex array of mechanisms, like the engine in an automobile, but the user (driver) does not need to know the inner workings of the engine in order to realize its tremendous benefits. LION's adoption will create a prairie fire of innovation which will reach most businesses in the next decades. Businesses, like plants in wildfire-prone ecosystems, will survive and prosper by adapting and embracing LION techniques, or they risk being transformed from giant trees to ashes by the spreading competition.

The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas.

This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties.

About the Authors
  • Roberto Battiti combines research and teaching in machine learning and optimization with a passion for startups and real-world applications. He received the Laurea from the University of Trento and the Ph. D. degree from the California Institute of Technology (Caltech), USA, in 1990. He is full professor of Computer Science and director of the LION lab for machine Learning and Intelligent OptimizatioN. He is a Fellow of the IEEE (class of 2009).
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