Название: Evolutionary Machine Learning Techniques: Algorithms and Applications Автор: Seyedali Mirjalili, Hossam Faris
Издательство: Springer
Год: 2020
Страниц: 287
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
This book provides an in-depth analysis of the current evolutionary Machine Learning (ML) techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks.
The field of Artificial Intelligence (AI) has become incredibly popular in the last decade. In the past five years, leading information companies largely invested in this area as reliable solutions to solve business problems in a wide range of industries. Governments have increased funding for AI research centres across the globe as well. AI is a broad field and can be divided into several branches:
1. Search methods
2. Machine learning
3. Knowledge representation and reasoning
4. Machine vision
5. Natural Language Processing
6. Robotics
The book provides essential definitions, literature reviews, and the training algorithms for Machine Learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
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