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130531s2013 gw | s |||| 0|eng d |
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|a 9783642386527
|9 978-3-642-38652-7
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|a 10.1007/978-3-642-38652-7
|2 doi
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|a TA329-348
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|a MAT003000
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|a 519
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|a Kramer, Oliver.
|e author.
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|a Dimensionality Reduction with Unsupervised Nearest Neighbors
|c by Oliver Kramer.
|h [electronic resource] /
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2013.
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| 300 |
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|a XII, 132 p. 48 illus., 45 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a text file
|b PDF
|2 rda
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|a Intelligent Systems Reference Library,
|v 51
|x 1868-4394 ;
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|a Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions.
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|a 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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|a Engineering.
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|a Artificial intelligence.
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|a Engineering mathematics.
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|a Operations research.
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|a Engineering.
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|a Appl.Mathematics/Computational Methods of Engineering.
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|a Artificial Intelligence (incl. Robotics).
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| 650 |
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|a Operation Research/Decision Theory.
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783642386510
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| 830 |
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|a Intelligent Systems Reference Library,
|v 51
|x 1868-4394 ;
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| 856 |
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|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-38652-7
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|a ZDB-2-ENG
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|a Engineering (Springer-11647)
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