Dimensionality reduction with unsupervised nearest neighbors
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsuperv...
Zapisane w:
| 1. autor: | |
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| Format: | Livre numérique |
| Język: | Anglais |
| Wydane: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Wydanie: | 1st ed. 2013. |
| Seria: | Intelligent Systems Reference Library
51 |
| Dostęp online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Komentarz: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Dimensionality Reduction with Unsupervised Nearest Neighbors, Texte imprimé, 9783642386510 • Dimensionality Reduction with Unsupervised Nearest Neighbors, Texte imprimé, 9783642386510 • Dimensionality Reduction with Unsupervised Nearest Neighbors, Texte imprimé, 9783642386534 • Dimensionality Reduction with Unsupervised Nearest Neighbors, Texte imprimé, 9783662518953 • Dimensionality Reduction with Unsupervised Nearest Neighbors, Texte imprimé, 9783642386510 • Dimensionality Reduction with Unsupervised Nearest Neighbors, Texte imprimé, 9783642386534 • Dimensionality Reduction with Unsupervised Nearest Neighbors, Texte imprimé, 9783662518953 |
| Streszczenie: | This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results. |
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| Deskrypcja: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783642386527 |
| ISSN: | 1868-4408 |
| Ograniczenie dostępu: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |