TY - BOOK ID - 48211733 TI - Nature-Inspired Computation in Data Mining and Machine Learning AU - Yang, Xin-She. AU - He, Xing-Shi. PY - 2020 SN - 3030285537 3030285529 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Engineering. KW - Artificial intelligence. KW - Data mining. KW - Computational Intelligence. KW - Artificial Intelligence. KW - Data Mining and Knowledge Discovery. KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Natural computation. KW - Machine learning. KW - Learning, Machine KW - Artificial intelligence KW - Biologically-inspired computing KW - Bio-inspired computing KW - Natural computing KW - Computational intelligence. KW - Intelligence, Computational KW - Soft computing UR - https://www.unicat.be/uniCat?func=search&query=sysid:48211733 AB - 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. ER -