Empirical Approach to Machine Learning

• Main Authors: Angelov, Plamen P. (Author, http://id.loc.gov/vocabulary/relators/aut), Gu, Xiaowei (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Studies in Computational Intelligence, 800
• Subjects: 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...

Table of Contents:

• 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.