Introduction

Angelov, P.P. and Gu, X. (2019) Introduction. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, 800 . Springer-Verlag, pp. 1-14. ISBN 9783030023836

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Abstract

Today we live in a data-rich environment. This is dramatically different from the last century when the fundamentals of machine learning, control theory and related subjects were established. Nowadays, vast and exponentially increasing data sets and streams which are often non-linear, non-stationary and increasingly more multi-modal/heterogeneous (combining various physical variables, signals with images/videos as well as text) are being generated, transmitted and recorded as a result of our everyday live. This is drastically different from the reality when the fundamental results of the probability theory, statistics and statistical learning where developed few centuries ago. © 2019, Springer Nature Switzerland AG.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
129541
Deposited By:
Deposited On:
08 Jan 2019 15:45
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Mar 2020 10:00