A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Revisiting the central and peripheral immune system
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
On the use of hyperspheres in artificial immune systems as antibody recognition regions
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Structure versus function: a topological perspective on immune networks
Natural Computing: an international journal
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Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, ormodelling biologically plausible dynamical systems, with little overlap between. Although the balance is latterly beginning to be redressed (e.g. [18]), we propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction. This paper outlines how an inappropriate interpretation of Perelson's shape-space formalism has largely contributed to this dichotomy, as it neither scales to machine-learning requirements nor makes any operational distinction between signals and context.We illustrate these issues and attempt to derive both a more biologically plausible and statistically solid foundation for an online, unsupervised artificial immune system. By extending a mathematical model of immunological tolerance, and grounding it in contemporary machine learning, we minimise any recourse to "reasoning by metaphor" and demonstrate one view of how both research agendas might still complement each other.