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Titlebook: Lectures on Intelligent Systems; Leonardo Vanneschi,Sara Silva Textbook 2023 Springer Nature Switzerland AG 2023 Optimization.Computationa

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发表于 2025-3-21 16:58:06 | 显示全部楼层 |阅读模式
书目名称Lectures on Intelligent Systems
编辑Leonardo Vanneschi,Sara Silva
视频video
概述Provides the reader with an essential understanding of intelligent systems.Does not describe applications and instead focuses on computational methods.Discusses optimization problems and machine learn
丛书名称Natural Computing Series
图书封面Titlebook: Lectures on Intelligent Systems;  Leonardo Vanneschi,Sara Silva Textbook 2023 Springer Nature Switzerland AG 2023 Optimization.Computationa
描述.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 treelearning, 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-containe
出版日期Textbook 2023
关键词Optimization; Computational Intelligence; Artificial Intelligence; Evolutionary Computing; Machine Learn
版次1
doihttps://doi.org/10.1007/978-3-031-17922-8
isbn_softcover978-3-031-17924-2
isbn_ebook978-3-031-17922-8Series ISSN 1619-7127 Series E-ISSN 2627-6461
issn_series 1619-7127
copyrightSpringer Nature Switzerland AG 2023
The information of publication is updating

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Introduction,d implementing a solution. In this context, a problem can be defined as a task that has to be fulfilled automatically, and solving a problem typically implies the design and development of an algorithm
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Particle Swarm Optimizationnt to land. In such a situation, defining where the whole swarm should land is a complex problem, since it depends on many pieces of information, such as, for instance, maximizing the availability of food or minimizing the risk of existence of predators.
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Artificial Neural Networkspses. The brain learns because neurons are able to communicate with each other. A picture of a biological neuron and its synapses is shown in Figure 7.1. Biological neurons can receive stimuli and, as a consequence, emit (electric) signals, which can stimulate other neurons. When a biological neuron emits its signal, we say that it “fires”.
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Genetic Programmingical of computer programs, they do not provide any convenient way of incorporating iteration and recursion, and so on. But above all, GA representation schemes do not have any dynamic variability: the initial selection of string length limits in advance the number of internal states of the system and limits what the system can learn.
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Support Vector Machinestive of maximizing classification accuracy and robustness, and generalization ability. In this chapter, SVMs are first introduced for binary classification and for linearly separable problems. Then, the concepts are extended to nonlinearly separable problems and multiclass classification.
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Unsupervised Learning: Clustering Algorithmsata itself as the expected output, and therefore can also be regarded as supervised learning. We do not cover autoencoders or any other unsupervised method whose goal is not to split the data into different groups.
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