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Machine learning in biomedical engineering

Machine learning in biomedical engineering Biomedical Engineering Letters (2018) 8:1–3 https://doi.org/10.1007/s13534-018-0058-3(0123456789().,-volV)(0123456789().,-volV) EDITORIAL 1 2 3 • • Cheolsoo Park Clive Cheong Took Joon-Kyung Seong Received: 19 January 2018 / Revised: 22 January 2018 / Accepted: 22 January 2018 / Published online: 6 February 2018 Korean Society of Medical and Biological Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Machine learning, which was first paraphrased by Arthur areas have given rise to a new research dimension, and the Samuel, can be defined as a field of computer science that increasing size of biomedical data requires precise machine gives computers the ability to learn without being explicitly learning-based data mining algorithms. programmed [1]. Having evolved from the study of pattern This special issue ‘‘Machine Learning in Biomedical recognition and computational learning theory in artificial Engineering’’ tries to capture the scope of various appli- intelligence [2], machine learning creates algorithms that cations of machine learning in the biomedical engineering can learn from a large body of data and make predictions field, with a special emphasis on the most representative on the data [3]. Machine learning is applied to a wide range machine learning techniques, namely deep learning-based of computing tasks, such as email filtering, detection http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering Letters Springer Journals

Machine learning in biomedical engineering

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Korean Society of Medical and Biological Engineering and Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Biomedical Engineering; Biological and Medical Physics, Biophysics; Biomedicine, general; Medical and Radiation Physics
ISSN
2093-9868
eISSN
2093-985X
DOI
10.1007/s13534-018-0058-3
Publisher site
See Article on Publisher Site

Abstract

Biomedical Engineering Letters (2018) 8:1–3 https://doi.org/10.1007/s13534-018-0058-3(0123456789().,-volV)(0123456789().,-volV) EDITORIAL 1 2 3 • • Cheolsoo Park Clive Cheong Took Joon-Kyung Seong Received: 19 January 2018 / Revised: 22 January 2018 / Accepted: 22 January 2018 / Published online: 6 February 2018 Korean Society of Medical and Biological Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Machine learning, which was first paraphrased by Arthur areas have given rise to a new research dimension, and the Samuel, can be defined as a field of computer science that increasing size of biomedical data requires precise machine gives computers the ability to learn without being explicitly learning-based data mining algorithms. programmed [1]. Having evolved from the study of pattern This special issue ‘‘Machine Learning in Biomedical recognition and computational learning theory in artificial Engineering’’ tries to capture the scope of various appli- intelligence [2], machine learning creates algorithms that cations of machine learning in the biomedical engineering can learn from a large body of data and make predictions field, with a special emphasis on the most representative on the data [3]. Machine learning is applied to a wide range machine learning techniques, namely deep learning-based of computing tasks, such as email filtering, detection

Journal

Biomedical Engineering Letters – Springer Journals

Published: Feb 6, 2018

References

  • Some studies in machine learning using the game of checkers
    Samuel, A
  • Glossary of terms
    Kohavi, R; Provost, F