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Empirical Approach to Machine Learning

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Empirical Approach to Machine Learning. / Angelov, Plamen Parvanov; Gu, Xiaowei.
Cham: Springer, 2019. 437 p. (Studies in Computational Intelligence; Vol. 800).

Research output: Book/Report/Proceedings › Book

Harvard

Angelov, PP & Gu, X 2019, Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol. 800, Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3

APA

Angelov, P. P., & Gu, X. (2019). Empirical Approach to Machine Learning. (Studies in Computational Intelligence; Vol. 800). Springer. https://doi.org/10.1007/978-3-030-02384-3

Vancouver

Angelov PP, Gu X. Empirical Approach to Machine Learning. Cham: Springer, 2019. 437 p. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-02384-3

Author

Angelov, Plamen Parvanov ; Gu, Xiaowei. / Empirical Approach to Machine Learning. Cham : Springer, 2019. 437 p. (Studies in Computational Intelligence).

Bibtex

@book{865307a3ea434596b95fbf14bb25fc37,
title = "Empirical Approach to Machine Learning",
abstract = "This book provides a {\textquoteleft}one-stop source{\textquoteright} for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today{\textquoteright}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.",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3",
language = "English",
isbn = "9783030023836",
series = "Studies in Computational Intelligence",
publisher = "Springer",

}

RIS

TY - BOOK

T1 - Empirical Approach to Machine Learning

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - http://www.research.lancs.ac.uk/portal/en/publications/empirical-approach-to-machine-learning(865307a3-ea43-4596-b95f-bf14bb25fc37).html

U2 - 10.1007/978-3-030-02384-3

DO - 10.1007/978-3-030-02384-3

M3 - Book

SN - 9783030023836

T3 - Studies in Computational Intelligence

BT - Empirical Approach to Machine Learning

PB - Springer

CY - Cham

ER -