000			03390nam a22003855i 4500
001			393822
003			ES-MaUEC
005			20230102123031.0
007			cr nn 008mamaa
008			220826s2022 sz | s |||| 0|eng d
020			_a9783031154447
024	7		_a10.1007/978-3-031-15444-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aAdvances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization _bDedicated to the Memory of Teuvo Kohonen Proceedings of the 14th International Workshop, WSOM+ 2022, Prague, Czechia, July 6-7, 2022 _cedited by Jan Faigl, Madalina Olteanu, Jan Drchal
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XII, 119 páginas) _b45 ilustraciones, 34 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aLecture Notes in Networks and Systems _x2367-3389 _v533
505	0		_aSparse 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.
520			_aIn 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.
700	1		_aFaigl, Jan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aOlteanu, Madalina _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aDrchal, Jan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783031154430
776	0	8	_iPrinted edition: _z9783031154454
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-15444-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Robotics_2022
999			_c393822 _d393822