| dc.creator | Nanas, N. | en |
| dc.creator | Kodovas, S. | en |
| dc.creator | Vavalis, M. | en |
| dc.creator | Houstis, E. | en |
| dc.date.accessioned | 2015-11-23T10:40:29Z | |
| dc.date.available | 2015-11-23T10:40:29Z | |
| dc.date.issued | 2010 | |
| dc.identifier | 10.1007/978-3-642-14547-6_5 | |
| dc.identifier.isbn | 3642145469 | |
| dc.identifier.issn | 3029743 | |
| dc.identifier.uri | http://hdl.handle.net/11615/31268 | |
| dc.description.abstract | Adaptive Information Filtering is a challenging computational problem that requires a high dimensional feature space. However, theoretical issues arise when vector-based representations are adopted in such a space. In this paper, we use AIF as a test bed to provide experimental evidence indicating that the learning abilities of vector-based Artificial Immune Systems are diminished in a high dimensional space. © 2010 Springer-Verlag Berlin Heidelberg. | en |
| dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-77955912769&partnerID=40&md5=e36a315409462a02a19384b8c29a4a74 | |
| dc.subject | Adaptive information | en |
| dc.subject | Artificial Immune System | en |
| dc.subject | Computational problem | en |
| dc.subject | Experimental evidence | en |
| dc.subject | High dimensional spaces | en |
| dc.subject | High-dimensional feature space | en |
| dc.subject | Information filtering | en |
| dc.subject | Learning abilities | en |
| dc.subject | Vector-based | en |
| dc.subject | Equipment testing | en |
| dc.subject | Immunology | en |
| dc.subject | Vector spaces | en |
| dc.title | Immune inspired information filtering in a high dimensional space | en |
| dc.type | other | en |
