Batch and on-line parameter estimation of Gaussian mixtures based on the joint entropy
Proceedings of the 1998 conference on Advances in neural information processing systems II
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Theoretical advances in artificial immune systems
Theoretical Computer Science
Adaptable Lymphocytes for Artificial Immune Systems
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
A solution concept for artificial immune networks: a coevolutionary perspective
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
A comprehensive benchmark of the artificial immune recognition system (AIRS)
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Theoretical Computer Science
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AIRS is an immune-inspired supervised learning algorithm that has been shown to perform competitively on some common datasets. Previous analysis of the algorithm consists almost exclusively of empirical benchmarks and the reason for its success remains somewhat speculative. In this paper, we decouple the statistical and immunological aspects of AIRS and consider their merits individually. This perspective allows us to clarifying why AIRS performs as it does and identify deficiencies that leave AIRS lacking. A comparison with Radial Basis Functions suggests that each may have something to offer the other.