Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/BMEI.2008.74 | BMEI |
Keywords | Field | DocType |
TIME-SERIES | Information theory,Time series modeling,Computer science,Minimum description length,Pulse (signal processing),Artificial intelligence,Artificial neural network | Conference |
Volume | ISSN | ISBN |
2 | 1948-2914 | 978-0-7695-3118-2 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying-Nan Ma | 1 | 4 | 2.06 |
| Yi Zhao | 2 | 121 | 31.91 |
| You-Hua Fan | 3 | 2 | 1.84 |
| Hu Hong | 4 | 2 | 5.31 |
| Xiujun Zhang | 5 | 159 | 18.75 |