On AIRS and Clonal Selection for Machine Learning [chapter]

Chris McEwan, Emma Hart
2009 Lecture Notes in Computer Science  
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
more » ... IRS lacking. A comparison with Radial Basis Functions suggests that each may have something to offer the other.
doi:10.1007/978-3-642-03246-2_11 fatcat:kq3oix24hja73iwoxdjvjzu7ue