Raykov, Yordan and Saad, David (2022). Principled machine learning. IEEE Journal of Selected Topics in Quantum Electronics, 28 (4),
Abstract
We introduce the underlying concepts which give rise to some of the commonly used machine learning methods, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonics. The main methods covered include parametric and non-parametric regression and classification techniques, kernel-based methods and support vector machines, decision trees, probabilistic models, Bayesian graphs, mixture models, Gaussian processes, message passing methods and visual informatics.
Publication DOI: | https://doi.org/10.1109/JSTQE.2022.3186798 |
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Divisions: | College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE) College of Engineering & Physical Sciences > Systems analytics research institute (SARI) College of Engineering & Physical Sciences Aston University (General) |
Additional Information: | UKRI Rights Retention: For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising Funding: DS acknowledges support from the EPSRC Programme Grant TRANSNET (EP/R035342/1) and the Leverhulme trust (RPG-2018-092). YR acknowledges support by the EPSRC Horizon Digital Economy Research grant ‘Trusted Data Driven Products: EP/T022493/1 and grant ‘From Human Data to Personal Experience’: EP/M02315X/1. |
Uncontrolled Keywords: | Channel estimation,Computational modeling,Kernel,Machine learning,Neural networks,Probabilistic logic,Statistical machine learning,Visualization,deciion trees,dimensionality reduction,kernel-based methods,message passing techniques,probabilistic methods,visual informatics,Atomic and Molecular Physics, and Optics,Electrical and Electronic Engineering |
Publication ISSN: | 1077-260X |
Last Modified: | 06 Dec 2024 08:27 |
Date Deposited: | 30 Jun 2022 08:01 |
Full Text Link: | |
Related URLs: |
https://ieeexpl ... ocument/9808310
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2022-07-31 |
Published Online Date: | 2022-06-27 |
Accepted Date: | 2022-06-23 |
Authors: |
Raykov, Yordan
Saad, David ( 0000-0001-9821-2623) |