Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
A comparative analysis of artificial immune network models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Decentralized control system for autonomous navigation based on an evolved artificial immune network
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A solution concept for artificial immune networks: a coevolutionary perspective
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
This paper presents a new artificial immune network model that addresses the problem of non-parametric density estimation. The model combines immune ideas with the known Parzen window estimator. The model uses a general representation of antibodies, which leads to redefine the network dynamics. The model is able to perform on-line learning, that is to say, training samples are presented only once. Results from exploratory experiments are presented in order to give insights on the reliability of the estimations of the proposed model.