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20231223081045.0 |
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231223s2005 xx ||||| 00| ||eng c |
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|a (DE-627)NLM157866548
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|a (NLM)16173183
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Meinicke, Peter
|e verfasserin
|4 aut
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| 245 |
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|a Principal surfaces from unsupervised kernel regression
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|c 2005
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 12.10.2005
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a We propose a nonparametric approach to learning of principal surfaces based on an unsupervised formulation of the Nadaraya-Watson kernel regression estimator. As compared with previous approaches to principal curves and surfaces, the new method offers several advantages: First, it provides a practical solution to the model selection problem because all parameters can be estimated by leave-one-out cross-validation without additional computational cost. In addition, our approach allows for a convenient incorporation of nonlinear spectral methods for parameter initialization, beyond classical initializations based on linear PCA. Furthermore, it shows a simple way to fit principal surfaces in general feature spaces, beyond the usual data space setup. The experimental results illustrate these convenient features on simulated and real data
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|a Evaluation Study
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
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|a Klanke, Stefan
|e verfasserin
|4 aut
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| 700 |
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|a Memisevic, Roland
|e verfasserin
|4 aut
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| 700 |
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|a Ritter, Helge
|e verfasserin
|4 aut
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| 773 |
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8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 27(2005), 9 vom: 08. Sept., Seite 1379-91
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:27
|g year:2005
|g number:9
|g day:08
|g month:09
|g pages:1379-91
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|a AR
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|d 27
|j 2005
|e 9
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|h 1379-91
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