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rdf:type
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Description
| - This paper deals with a comparison of statistical and non-statistical classifiers for thumb motion classification. Presented work is a part of research of relation between brain and muscle activity. The thumb motion is represented by trajectory coordinates of special mark placed on the thumb. Motions are classified using k-Means classifier (non-statistical classification, no prior information is needed) and Bayes classifier (statistical models of classes are needed, classifier training is necessary). The efficiency of classifiers is evaluated using the standard HTK parameters. Real testing data includes more than 900 stationary states which are used for classifier testing. The classification results are compared for both methods.
- This paper deals with a comparison of statistical and non-statistical classifiers for thumb motion classification. Presented work is a part of research of relation between brain and muscle activity. The thumb motion is represented by trajectory coordinates of special mark placed on the thumb. Motions are classified using k-Means classifier (non-statistical classification, no prior information is needed) and Bayes classifier (statistical models of classes are needed, classifier training is necessary). The efficiency of classifiers is evaluated using the standard HTK parameters. Real testing data includes more than 900 stationary states which are used for classifier testing. The classification results are compared for both methods. (en)
- This paper deals with a comparison of statistical and non-statistical classifiers for thumb motion classification. Presented work is a part of research of relation between brain and muscle activity. The thumb motion is represented by trajectory coordinates of special mark placed on the thumb. Motions are classified using k-Means classifier (non-statistical classification, no prior information is needed) and Bayes classifier (statistical models of classes are needed, classifier training is necessary). The efficiency of classifiers is evaluated using the standard HTK parameters. Real testing data includes more than 900 stationary states which are used for classifier testing. The classification results are compared for both methods. (cs)
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Title
| - Comparison of Statistical and Non-statistical Classifiers for Thumb Motion Clasification
- Comparison of Statistical and Non-statistical Classifiers for Thumb Motion Clasification (en)
- Comparison of Statistical and Non-statistical Classifiers for Thumb Motion Clasification (cs)
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skos:prefLabel
| - Comparison of Statistical and Non-statistical Classifiers for Thumb Motion Clasification
- Comparison of Statistical and Non-statistical Classifiers for Thumb Motion Clasification (en)
- Comparison of Statistical and Non-statistical Classifiers for Thumb Motion Clasification (cs)
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skos:notation
| - RIV/68407700:21230/08:03144957!RIV09-MSM-21230___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...iv/cisloPeriodika
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/08:03144957
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - motion analysis, motion classification, k-Means classification, Bayes classification (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...odStatuVydavatele
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http://linked.open...ontrolniKodProRIV
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http://linked.open...i/riv/nazevZdroje
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...v/svazekPeriodika
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http://linked.open...iv/tvurceVysledku
| - Havlík, Jan
- Horčík, Zdeněk
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http://linked.open...n/vavai/riv/zamer
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issn
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number of pages
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http://localhost/t...ganizacniJednotka
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