Natural Computing for Unsupervised Learning

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Li, Xiangtao (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Wong, Ka-Chun (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Unsupervised and Semi-Supervised Learning,
• Θέματα: Electrical engineering.; Signal processing.; Image processing.; Speech processing systems.; Pattern recognition.; Artificial intelligence.; Data mining.; Communications Engineering, Networks.; Signal, Image and Speech Processing.; Pattern Recognition.; Artificial Intelligence.; Data Mining and Knowledge Discovery.

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes...

Πίνακας περιεχομένων:

• Introduction
• Part I - Basic Natural Computing Techniques for Unsupervised Learning
• Hard Clustering using Evolutionary Algorithms
• Soft Clustering using Evolutionary Algorithms
• Fuzzy / Rough Set Systems for Unsupervised Learning
• Unsupervised Feature Selection using Evolutionary Algorithms
• Unsupervised Feature Selection using Artificial Neural Networks
• Part II - Advanced Natural Computing Techniques for Unsupervised Learning
• Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering
• Nature-Inspired Optimization Approaches for Unsupervised Feature Selection
• Co-Evolutionary Approaches for Unsupervised Learning
• Mining Evolving Patterns using Natural Computing Techniques
• Multi-objective Optimization for Unsupervised Learning
• Many-objective Optimization for Unsupervised Learning
• Part III - Applications
• Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques
• Parallel Solution-based Natural Clustering Techniques on Railway Engineering data
• Natural Computing Techniques for Community Detection on Online Social Networks
• Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning
• Conclusion.