Name
Abstract | ||
|---|---|---|
This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithm's performance on two types of anomaly detection problem. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.tcs.2012.10.057 | Theor. Comput. Sci. |
Keywords | DocType | Volume |
anomaly detection,Receptor Density Algorithm,exponential smoothing,anomaly detection problem,theoretical foundation,new Artificial Immune System,kernel density estimation | Journal | 481, |
ISSN | Citations | PageRank |
0304-3975 | 4 | 0.41 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nick D. L. Owens | 1 | 116 | 9.18 |
| Andrew J. Greensted | 2 | 77 | 7.39 |
| Jon Timmis | 3 | 1237 | 120.32 |
| Andy Tyrrell | 4 | 158 | 13.74 |