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Dimensionality Reduction with Unsupervised Nearest Neighbors (Paperback, Softcover reprint of the original 1st ed. 2013) Loot Price: R2,927
Discovery Miles 29 270
Dimensionality Reduction with Unsupervised Nearest Neighbors (Paperback, Softcover reprint of the original 1st ed. 2013):...

Dimensionality Reduction with Unsupervised Nearest Neighbors (Paperback, Softcover reprint of the original 1st ed. 2013)

Oliver Kramer

Series: Intelligent Systems Reference Library, 51

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Loot Price R2,927 Discovery Miles 29 270 | Repayment Terms: R274 pm x 12*

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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.  

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Intelligent Systems Reference Library, 51
Release date: April 2017
First published: 2013
Authors: Oliver Kramer
Dimensions: 235 x 155 x 8mm (L x W x T)
Format: Paperback
Pages: 132
Edition: Softcover reprint of the original 1st ed. 2013
ISBN-13: 978-3-662-51895-3
Categories: Books > Business & Economics > General
Books > Computing & IT > General
Books > Professional & Technical > Technology: general issues > General
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LSN: 3-662-51895-3
Barcode: 9783662518953

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