Neuware -In this collection, the reader can �nd recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional �elds of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, speci�cally those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 132 pp. Englisch.

N� de ref. del art�culo 9783031154430

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Detalles bibliogr�ficos

T�tulo: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
Editor: Springer International Publishing, Springer International Publishing Aug 2022
Fecha de publicaci�n: 2022
Idioma: Ingl�s
ISBN 10: 3031154436
ISBN 13: 9783031154430
Encuadernaci�n: Taschenbuch
Estado: Neu
Dimensiones: 235�mil�metros de ancho por 155 de alto por 8 de largo
Peso: 213 gramos

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Sinopsis

In this collection, the reader can ﬁnd recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional ﬁelds of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, speciﬁcally those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.

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In this collection, the reader can ?nd recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional ?elds of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, speci?cally those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.

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