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A Survey of Topological Machine Learning Methods.
Hensel, Felix; Moor, Michael; Rieck, Bastian.
Afiliação
  • Hensel F; Machine Learning and Computational Biology Laboratory, ETH Zurich, Zurich, Switzerland.
  • Moor M; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
  • Rieck B; Machine Learning and Computational Biology Laboratory, ETH Zurich, Zurich, Switzerland.
Front Artif Intell ; 4: 681108, 2021.
Article em En | MEDLINE | ID: mdl-34124648
The last decade saw an enormous boost in the field of computational topology: methods and concepts from algebraic and differential topology, formerly confined to the realm of pure mathematics, have demonstrated their utility in numerous areas such as computational biology personalised medicine, and time-dependent data analysis, to name a few. The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA). Next to their applications in the aforementioned areas, TDA methods have also proven to be effective in supporting, enhancing, and augmenting both classical machine learning and deep learning models. In this paper, we review the state of the art of a nascent field we refer to as "topological machine learning," i.e., the successful symbiosis of topology-based methods and machine learning algorithms, such as deep neural networks. We identify common threads, current applications, and future challenges.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article