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Introduction

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)

Published
Publication date2019
Host publicationEmpirical Approach to Machine Learning
EditorsPlamen Angelov, Xiaowei Gu
PublisherSpringer-Verlag
Pages1-14
Number of pages14
Volume800
ISBN (Print)9783030023836
Original languageEnglish

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
Volume800
ISSN (Print)1860-949X

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.