Principled machine learning

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
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
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
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
Full Text Link:
Related URLs: https://ieeexpl ... ocument/9808310 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-06-27
Published Online Date: 2022-06-27
Accepted Date: 2022-06-23
Authors: Raykov, Yordan
Saad, David (ORCID Profile 0000-0001-9821-2623)

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