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|a (DE-627)NLM150513151
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|a (NLM)15376902
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Keysers, Daniel
|e verfasserin
|4 aut
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|a Adaptation in statistical pattern recognition using tangent vectors
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|c 2004
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 12.10.2004
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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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|a We integrate the tangent method into a statistical framework for classification analytically and practically. The resulting consistent framework for adaptation allows us to efficiently estimate the tangent vectors representing the variability. The framework improves classification results on two real-world pattern recognition tasks from the domains handwritten character recognition and automatic speech recognition
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|a Comparative Study
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|a Evaluation Study
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|a Journal Article
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|a Validation Study
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|a Macherey, Wolfgang
|e verfasserin
|4 aut
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|a Ney, Hermann
|e verfasserin
|4 aut
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|a Dahmen, Jörg
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1998
|g 26(2004), 2 vom: 24. Feb., Seite 269-74
|w (DE-627)NLM098212257
|x 0162-8828
|7 nnns
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|g volume:26
|g year:2004
|g number:2
|g day:24
|g month:02
|g pages:269-74
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|a GBV_ILN_350
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|a AR
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|d 26
|j 2004
|e 2
|b 24
|c 02
|h 269-74
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