Name
Abstract | ||
|---|---|---|
The T cell is able to perform fine-grained anomaly detection via its T Cell Receptor and intracellular signalling networks. We abstract from models of T Cell signalling to develop a new Artificial Immune System concepts involving the internal components of the TCR. We show that the concepts of receptor signalling have a natural interpretation as Parzen Window Kernel Density Estimation applied to anomaly detection. We then demonstrate how the dynamic nature of the receptors allows anomaly detection when probability distributions vary in time. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03246-2_15 | ICARIS |
Keywords | Field | DocType |
artificial immune system,anomaly detection,t cell receptor,probability distribution,kernel density estimate,cell signalling | T-cell receptor,Anomaly detection,Artificial immune system,Signalling,Computer science,Receptor,Probability distribution,Artificial intelligence,T cell,Machine learning,Kernel density estimation | Conference |
Volume | ISSN | Citations |
5666 | 0302-9743 | 12 |
PageRank | References | Authors |
0.92 | 4 | 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 |
| Andrew M. Tyrrell | 4 | 326 | 49.07 |