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Brief Introduction to Statistical Machine Learning

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Brief Introduction to Statistical Machine Learning. / Angelov, P.P.; Gu, X.

Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 17-67 (Studies in Computational Intelligence; Vol. 800).

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

Harvard

Angelov, PP & Gu, X 2019, Brief Introduction to Statistical Machine Learning. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 17-67. https://doi.org/10.1007/978-3-030-02384-3_2

APA

Angelov, P. P., & Gu, X. (2019). Brief Introduction to Statistical Machine Learning. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 17-67). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_2

Vancouver

Angelov PP, Gu X. Brief Introduction to Statistical Machine Learning. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 17-67. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-02384-3_2

Author

Angelov, P.P. ; Gu, X. / Brief Introduction to Statistical Machine Learning. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 17-67 (Studies in Computational Intelligence).

Bibtex

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title = "Brief Introduction to Statistical Machine Learning",
abstract = "In this chapter, an overview of the theory of probability, statistical and machine learning is made covering the main ideas and the most popular and widely used methods in this area. As a starting point, the randomness and determinism as well as the nature of the real-world problems are discussed. Then, the basic and well-known topics of the traditional probability theory and statistics including the probability mass and distribution, probability density and moments, density estimation, Bayesian and other branches of the probability theory, are recalled followed by a analysis. The well-known data pre-processing techniques, unsupervised and supervised machine learning methods are covered. These include a brief introduction of the distance metrics, normalization and standardization, feature selection, orthogonalization as well as a review of the most representative clustering, classification, regression and prediction approaches of various types. In the end, the topic of image processing is also briefly covered including the popular image transformation techniques, and a number of image feature extraction techniques at three different levels. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_2",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "17--67",
editor = "Plamen Angelov and Xiaowei Gu",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Brief Introduction to Statistical Machine Learning

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - In this chapter, an overview of the theory of probability, statistical and machine learning is made covering the main ideas and the most popular and widely used methods in this area. As a starting point, the randomness and determinism as well as the nature of the real-world problems are discussed. Then, the basic and well-known topics of the traditional probability theory and statistics including the probability mass and distribution, probability density and moments, density estimation, Bayesian and other branches of the probability theory, are recalled followed by a analysis. The well-known data pre-processing techniques, unsupervised and supervised machine learning methods are covered. These include a brief introduction of the distance metrics, normalization and standardization, feature selection, orthogonalization as well as a review of the most representative clustering, classification, regression and prediction approaches of various types. In the end, the topic of image processing is also briefly covered including the popular image transformation techniques, and a number of image feature extraction techniques at three different levels. © 2019, Springer Nature Switzerland AG.

AB - In this chapter, an overview of the theory of probability, statistical and machine learning is made covering the main ideas and the most popular and widely used methods in this area. As a starting point, the randomness and determinism as well as the nature of the real-world problems are discussed. Then, the basic and well-known topics of the traditional probability theory and statistics including the probability mass and distribution, probability density and moments, density estimation, Bayesian and other branches of the probability theory, are recalled followed by a analysis. The well-known data pre-processing techniques, unsupervised and supervised machine learning methods are covered. These include a brief introduction of the distance metrics, normalization and standardization, feature selection, orthogonalization as well as a review of the most representative clustering, classification, regression and prediction approaches of various types. In the end, the topic of image processing is also briefly covered including the popular image transformation techniques, and a number of image feature extraction techniques at three different levels. © 2019, Springer Nature Switzerland AG.

U2 - 10.1007/978-3-030-02384-3_2

DO - 10.1007/978-3-030-02384-3_2

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 17

EP - 67

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -