Druck auf Anfrage Neuware - Printed after ordering - 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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Detalles bibliogr�ficos

T�tulo: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization : Dedicated to the Memory of Teuvo Kohonen / Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022
Editor: Springer International Publishing
Fecha de publicaci�n: 2022
Idioma: Ingl�s
ISBN 10: 3031154436
ISBN 13: 9783031154430
Encuadernaci�n: Taschenbuch
Estado: Neu

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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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