Faigl / Olteanu / Drchal | Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization | E-Book | sack.de
E-Book

E-Book, Englisch, Band 533, 119 Seiten, eBook

Reihe: Lecture Notes in Networks and Systems

Faigl / Olteanu / Drchal 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
1. Auflage 2022
ISBN: 978-3-031-15444-7
Verlag: Springer International Publishing
Format: PDF
 Kopierschutz: 1 - PDF Watermark

Dedicated to the Memory of Teuvo Kohonen / Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022

E-Book, Englisch, Band 533, 119 Seiten, eBook

Reihe: Lecture Notes in Networks and Systems

ISBN: 978-3-031-15444-7
Verlag: Springer International Publishing
Format: PDF
 Kopierschutz: 1 - PDF Watermark

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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Zielgruppe

Research

Autoren/Hrsg.

Faigl, Jan
Olteanu, Madalina
Drchal, Jan

Weitere Infos & Material

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.

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