Nature Inspired Computation in Data Mining and Machine Learning is popular PDF and ePub book, written by Xin-She Yang in 2019-09-03, it is a fantastic choice for those who relish reading online the Technology & Engineering genre. Let's immerse ourselves in this engaging Technology & Engineering book by exploring the summary and details provided below. Remember, Nature Inspired Computation in Data Mining and Machine Learning can be Read Online from any device for your convenience.
Nature Inspired Computation in Data Mining and Machine Learning Book PDF Summary
This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
Detail Book of Nature Inspired Computation in Data Mining and Machine Learning PDF
- Author : Xin-She Yang
- Release : 03 September 2019
- Publisher : Springer Nature
- ISBN : 9783030285531
- Genre : Technology & Engineering
- Total Page : 273 pages
- Language : English
- PDF File Size : 20,5 Mb
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