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Data Mining and Knowledge Discovery
Erschienen in: Data Mining and Knowledge Discovery 1/2021

17.11.2020

A survey of deep network techniques all classifiers can adopt

verfasst von: Alireza Ghods, Diane J. Cook

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 1/2021

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Abstract

Deep neural networks (DNNs) have introduced novel and useful tools to the machine learning community. Other types of classifiers can potentially make use of these tools as well to improve their performance and generality. This paper reviews the current state of the art for deep learning classifier technologies that are being used outside of deep neural networks. Non-neural network classifiers can employ many components found in DNN architectures. In this paper, we review the feature learning, optimization, and regularization methods that form a core of deep network technologies. We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification performance. Because many opportunities and challenges still exist, we discuss directions that can be pursued to expand the area of deep learning for a variety of classification algorithms.
Vorheriger Artikel A survey of community detection methods in multilayer networks
Nächster Artikel Online summarization of dynamic graphs using subjective interestingness for sequential data

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Metadaten
Titel
A survey of deep network techniques all classifiers can adopt
verfasst von
Alireza Ghods
Diane J. Cook
Publikationsdatum
17.11.2020
Verlag
Springer US
Erschienen in
Data Mining and Knowledge Discovery / Ausgabe 1/2021
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-020-00722-8

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