Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

Jan Faigl

ISBN 10: 3031154436 / ISBN 13: 9783031154430

Verlag: Springer International Publishing, Springer International Publishing Aug 2022, 2022

Beschreibung

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. Bestandsnummer des Verkäufers 9783031154430

Bibliografische Details

Titel: Advances in Self-Organizing Maps, Learning ...

Verlag: Springer International Publishing, Springer International Publishing Aug 2022

Erscheinungsdatum: 2022

Einband: Taschenbuch

Zustand: Neu

Über diesen Titel

Inhaltsangabe:

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.

Von der hinteren Coverseite:

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.