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Introduction

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

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Introduction. / Angelov, P.P.; Gu, X.
Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 1-14 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Angelov, PP & Gu, X 2019, Introduction. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 1-14. https://doi.org/10.1007/978-3-030-02384-3_1

APA

Angelov, P. P., & Gu, X. (2019). Introduction. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 1-14). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_1

Vancouver

Angelov PP, Gu X. Introduction. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 1-14. (Studies in Computational Intelligence). Epub 2018 Oct 18. doi: 10.1007/978-3-030-02384-3_1

Author

Angelov, P.P. ; Gu, X. / Introduction. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 1-14 (Studies in Computational Intelligence).

Bibtex

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title = "Introduction",
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. {\textcopyright} 2019, Springer Nature Switzerland AG.",
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volume = "800",
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booktitle = "Empirical Approach to Machine Learning",

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RIS

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AU - Gu, X.

PY - 2019

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AB - 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.

U2 - 10.1007/978-3-030-02384-3_1

DO - 10.1007/978-3-030-02384-3_1

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

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BT - Empirical Approach to Machine Learning

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