TY - GEN
T1 - Modeling the dynamics of the human pulse data by MDL-optimal neural networks
AU - Ma, Yingnan
AU - Zhao, Yi
AU - Fan, Youhua
AU - Hu, Hong
AU - Zhang, Xiujun
PY - 2008
Y1 - 2008
N2 - In this paper, we describe an information theoretic criterion, the method of minimum description length (MDL), to determine optimal neural networks to predict the human pulse data as well as non-stationary Lorenz data. Such optimal models which minimize the description length of the data both generalize well and accurately capture the dynamics of the original data. It demonstrates the potential utility of our MDL-optimal model in biomedical time series modeling.
AB - In this paper, we describe an information theoretic criterion, the method of minimum description length (MDL), to determine optimal neural networks to predict the human pulse data as well as non-stationary Lorenz data. Such optimal models which minimize the description length of the data both generalize well and accurately capture the dynamics of the original data. It demonstrates the potential utility of our MDL-optimal model in biomedical time series modeling.
UR - https://www.scopus.com/pages/publications/51649107874
U2 - 10.1109/BMEI.2008.74
DO - 10.1109/BMEI.2008.74
M3 - 会议稿件
AN - SCOPUS:51649107874
SN - 9780769531182
T3 - BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
SP - 460
EP - 463
BT - BioMedical Engineering and Informatics
T2 - BioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
Y2 - 27 May 2008 through 30 May 2008
ER -