Название: Optimization and Machine Learning: Optimization for Machine Learning and Machine Learning for OptimizationАвтор: Rachid Chelouah, Patrick Siarry
Издательство: Wiley
Год: 2022
Страниц: 255
Язык: английский
Формат: pdf (true), epub (true)
Размер: 18.1 MB
Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and Machine Learning, and to demonstrate how to apply them in the fields of engineering.
Machine Learning is revolutionizing our world. It is difficult to conceive of any other information technology that has developed so rapidly in recent years, in terms of real impact. The fields of machine learning and optimization are highly interwoven. Optimization problems form the core of Machine Learning methods and modern optimization algorithms are using Machine Learning more and more to improve their efficiency. Machine Learning has applications in all areas of science. There are many learning methods, each of which uses a different algorithmic structure to optimize predictions, based on the data received. Hence, the first objective of this book is to shed light on key principles and methods that are common within both fields. Machine Learning and optimization share three components: representation, evaluation and iterative search. Yet while optimization solvers are generally designed to be fast and accurate on implicit models, machine learning methods need to be generic and trained offline on datasets. Machine Learning problems present new challenges for optimization researchers, and Machine Learning practitioners seek simpler, generic optimization algorithms.
Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on Machine Learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on Machine Learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.
When conducting an audit, the ability to make use of all the available information relating to the audit universe or subject could improve the quality of results. Classifying text documents in the audit (unstructured data) could enable the use of additional information to improve existing structured data, leading to better knowledge to support the audit process. To provide better automated support for knowledge discovery, natural language processing (NLP) could be applied. This chapter compares the results of classical machine learning and deep learning algorithms, combined with advanced word embeddings in order to classify the findings of internal audit reports.
The design of a control architecture is a central problem in a project to realize an autonomous mobile robot. In the absence of a generic solution that overshadows all others, it is essential to come up with a new approach detailing the design process that will reach this goal. In this context, this chapter proposes to use the multi-agent paradigm and inference mechanism based on fuzzy logic in the design of the control architecture for the autonomous navigation of the mobile robot. The architecture developed must take into account imposed constraints (obstacle avoidance, no collisions with the walls of the environment, unknown environment, optimal path, reaching the goal), actor-based system multi-agents and distributed non-hierarchical architecture built around several agents (databases knowledge), cognitive and reactive, that cooperate with each other to solve the problem of autonomous navigation of the mobile robot. The method used allowed us to have an intelligent system capable of solving various problems produced during navigation.
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