Empirical Approach to Machine Learning

• Κύριοι συγγραφείς: Angelov, Plamen P. (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Gu, Xiaowei (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Studies in Computational Intelligence, 800
• Θέματα: Computational intelligence.; Pattern recognition.; Big data.; Data mining.; Computational complexity.; Computational Intelligence.; Pattern Recognition.; Big Data.; Data Mining and Knowledge Discovery.; Complexity.

This book provides a 'one-stop source' for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today's data-driven world. After an introduction to the fundamentals, the book discusses i...

Πίνακας περιεχομένων:

• Introduction
• Part I: Theoretical Background
• Brief Introduction to Statistical Machine Learning
• Brief Introduction to Computational Intelligence
• Part II: Theoretical Fundamentals of the Proposed Approach
• Empirical Approach - Introduction
• Empirical Fuzzy Sets and Systems
• Anomaly Detection - Empirical Approach
• Data Partitioning - Empirical Approach
• Autonomous Learning Multi-Model Systems
• Transparent Deep Rule-Based Classifiers
• Part III: Applications of the Proposed Approach
• Applications of Autonomous Anomaly Detection.