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Lectures on intelligent systems

Vanneschi, Leonardo.

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Lectures on intelligent systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Lectures on intelligent systems/ by Leonardo Vanneschi, Sara Silva.
作者:	Vanneschi, Leonardo.
其他作者:	Silva, Sara.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xiv, 349 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1: Introduction -- Chapter 2: Optimization Problems and Local Search -- Chapter 3: Genetic Algorithms -- Chapter 4: Particle Swarm Optimization -- Chapter 5: Introduction to Machine Learning -- Chapter 6: Decision Tree Learning -- Chapter 7: Artificial Neural Networks -- Chapter 8: Genetic Programming -- Bayesian Learning -- Chapter 10: Support Vector Machines -- Chapter 11: Ensemble Methods -- Chapter 12: Unsupervised Learning.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Mathematics. -
電子資源:	https://doi.org/10.1007/978-3-031-17922-8
ISBN:	9783031179228

Lectures on intelligent systems
Vanneschi, Leonardo.

Lectures on intelligent systems[electronic resource] /by Leonardo Vanneschi, Sara Silva. - Cham :Springer International Publishing :2023. - xiv, 349 p. :ill. (some col.), digital ;24 cm. - Natural computing series. - Natural computing series..

Chapter 1: Introduction -- Chapter 2: Optimization Problems and Local Search -- Chapter 3: Genetic Algorithms -- Chapter 4: Particle Swarm Optimization -- Chapter 5: Introduction to Machine Learning -- Chapter 6: Decision Tree Learning -- Chapter 7: Artificial Neural Networks -- Chapter 8: Genetic Programming -- Bayesian Learning -- Chapter 10: Support Vector Machines -- Chapter 11: Ensemble Methods -- Chapter 12: Unsupervised Learning.

This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications. The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision tree learning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning. This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners.

ISBN: 9783031179228

Standard No.: 10.1007/978-3-031-17922-8doiSubjects--Topical Terms:

813249
Artificial intelligence
--Mathematics.

LC Class. No.: Q335

Dewey Class. No.: 006.3

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520 $a This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications. The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision tree learning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning. This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners.
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