Nature-inspired computation in data mining and machine learning
Nature-inspired computation in data mining and machine learning
edited by Xin-She Yang, Xing-Shi He
- First edition
- 1 recurso en línea (XI, 273 páginas) 87 ilustraciones, 66 ilustraciones a color
- Studies in Computational Intelligence 855 1860-949X Intelligent Technologies and Robotics (Springer-42732) .
Adaptive Improved Flower Pollination Algorithm for Global Optimization -- Algorithms for Optimization and Machine Learning over Cloud -- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks -- Comparative analysis of different classifiers on crisis-related tweets: An elaborate study -- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm -- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services.
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.
9783030285531
10.1007/978-3-030-28553-1 doi
Data mining
Aprendizaje automático
Bioinformática
QA76.9 .N37 / 2020 EB
Adaptive Improved Flower Pollination Algorithm for Global Optimization -- Algorithms for Optimization and Machine Learning over Cloud -- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks -- Comparative analysis of different classifiers on crisis-related tweets: An elaborate study -- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm -- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services.
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.
9783030285531
10.1007/978-3-030-28553-1 doi
Data mining
Aprendizaje automático
Bioinformática
QA76.9 .N37 / 2020 EB