TY - GEN
T1 - Sleep classification with a combination of symbolic learning and learning vector quantization
AU - Pfurtscheller, Gert
AU - Flotzinger, Doris
AU - Kubat, Miroslav
PY - 1992/1/1
Y1 - 1992/1/1
N2 - Besides statistical methods, various Artificial Intelligence approaches can be used for sleep classification. Learning vector quantization (LVQ) and the top-down induction of decision trees (TDIDT) were applied on 8-hour sleep data from infants. It was shown that with a combination of TDIDT and LVQ the input dimension of the LVQ can be reduced without decreasing the classification accuracy. Classification accuracy was between 67 and 76%, depending on the infant.
AB - Besides statistical methods, various Artificial Intelligence approaches can be used for sleep classification. Learning vector quantization (LVQ) and the top-down induction of decision trees (TDIDT) were applied on 8-hour sleep data from infants. It was shown that with a combination of TDIDT and LVQ the input dimension of the LVQ can be reduced without decreasing the classification accuracy. Classification accuracy was between 67 and 76%, depending on the infant.
UR - http://www.scopus.com/inward/record.url?scp=84947002207&partnerID=8YFLogxK
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U2 - 10.1109/IEMBS.1992.5761661
DO - 10.1109/IEMBS.1992.5761661
M3 - Conference contribution
AN - SCOPUS:84947002207
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2748
EP - 2749
BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
A2 - Plonsey, Robert
A2 - Laxminarayan, Swamy
A2 - Coatrieux, Jean Louis
A2 - Morucci, Jean Pierre
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
Y2 - 29 October 1992 through 1 November 1992
ER -