Skip to main content

Empirical Approach to Machine Learning

Angelov, Plamen Parvanov, Gu, Xiaowei (2019) Empirical Approach to Machine Learning. Studies in Computational Intelligence . Springer, 423 pp. ISBN 978-3-030-02383-6. (doi:10.1007/978-3-030-02384-3) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90109)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1007/978-3-030-02384-3

Abstract

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.

Item Type: Book
DOI/Identification number: 10.1007/978-3-030-02384-3
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 09 Sep 2021 14:30 UTC
Last Modified: 10 Sep 2021 10:19 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90109 (The current URI for this page, for reference purposes)
  • Depositors only (login required):