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Advances in self-organizing maps, learning vector quantization, clustering and data visualizationdedicated to the memory of Teuvo Kohonen/Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in self-organizing maps, learning vector quantization, clustering and data visualizationedited by Jan Faigl, Madalina Olteanu, Jan Drchal.
Reminder of title:	dedicated to the memory of Teuvo Kohonen/Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022 /
remainder title:	WSOM+ 2022
other author:	Faigl, Jan.
corporate name:
Published:	Cham :Springer International Publishing :2022.
Description:	xii, 119 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science)
Online resource:	https://doi.org/10.1007/978-3-031-15444-7
ISBN:	9783031154447$q(electronic bk.)

Advances in self-organizing maps, learning vector quantization, clustering and data visualizationdedicated to the memory of Teuvo Kohonen/Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022 /
Advances in self-organizing maps, learning vector quantization, clustering and data visualizationdedicated to the memory of Teuvo Kohonen/Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022 /[electronic resource] :WSOM+ 2022edited by Jan Faigl, Madalina Olteanu, Jan Drchal. - Cham :Springer International Publishing :2022. - xii, 119 p. :ill. (some col.), digital ;24 cm. - Lecture notes in networks and systems,v. 5332367-3389 ;. - Lecture notes in networks and systems ;v.1..

Sparse weighted K-means for groups of mixed-type variables -- Fast parallel search of Best Matching Units in Self-Organizing Maps -- Neural networks for spatial models -- Machine Learning and Data-Driven Approaches in Spatial Statistics : a case study of housing price estimation -- Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory -- Inferring epsilon-nets of Finite Sets in a RKHS -- Steps Forward to Quantum Learning Vector Quantization for Classification Learning on a Theoretical Quantum Computer -- Application of Kohonen Maps in Predicting and Characterizing VAT Fraud in Southern Mozambique -- Visual insights from the latent space of generative models for molecular design.

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.

ISBN: 9783031154447$q(electronic bk.)

Standard No.: 10.1007/978-3-031-15444-7doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87 / .W67 2022

Dewey Class. No.: 006.32

Advances in self-organizing maps, learning vector quantization, clustering and data visualizationdedicated to the memory of Teuvo Kohonen/Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022 /
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