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Wir sind gerne für Sie daThis 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.
Produkteigenschaften
- Artikelnummer: 9783662518953
- Medium: Buch
- ISBN: 978-3-662-51895-3
- Verlag: Springer
- Erscheinungstermin: 30.04.2017
- Sprache(n): Englisch
- Auflage: Softcover Nachdruck of the original 1. Auflage 2013
- Serie: Intelligent Systems Reference Library
- Produktform: Kartoniert, Previously published in hardcover
- Gewicht: 454 g
- Seiten: 132
- Format (B x H): 155 x 235 mm
- Ausgabetyp: Kein, Unbekannt