Empirical Approach to Machine Learning

Empirical Approach to Machine Learning (Studies in Computational Intelligence)
By 作者: Plamen P. Angelov - Xiaowei Gu
ISBN-10 书号: 3030023834
ISBN-13 书号: 9783030023836
Edition 版本: 1st ed. 2019
Release Finelybook 出版日期: 2018-10-17
Pages 页数: (423 )

The Book Description robot was collected from Amazon and arranged by Finelybook

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 in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code.

1.Introduction Partl.Theoretical Background
2.Brief Introduction to Statistical Machine Learning
3.Brief Introduction to Computational Intelligence Partll.Theoretical Fundamentals of the Proposed Approach
4.Empirical Approach-Introduction
5.Empirical Fuzzy Sets and Systems
6.Anomaly Detection-Empirical Approach
7.Data Parttioning-Empirical Approach
8.Autonomous Learning Multi-model Systems9.Transparent Deep Rule-Based Classifiers Part ll.Applications of the Proposed Approach
10.Applications of Autonomous Anomaly Detection11.Applications of Autonomous Data Partitioning
12.Applications of Autonomous Learning Multi-model Systems
13.Applications of Deep Rule-Based Classifiers
14.Applications of Semi-supervised Deep Rule-Based Classifiers

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