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Clonal selection based artificial immune system for generalized pattern recognition

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dc.contributor.author Huntsberger, Terry
dc.date.accessioned 2013-10-11T16:00:17Z
dc.date.available 2013-10-11T16:00:17Z
dc.date.issued 2011-10-09
dc.identifier.citation IEEE International Conference on Systems, Man, and Cybernetics (SMC-IT), Anchorage, Alaska, October 9-12, 2011. en_US
dc.identifier.clearanceno 11-2836
dc.identifier.uri http://hdl.handle.net/2014/43830
dc.description.abstract The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011. en_US
dc.subject artificial immune system en_US
dc.subject pattern recognition en_US
dc.subject classification en_US
dc.title Clonal selection based artificial immune system for generalized pattern recognition en_US
dc.type Preprint en_US


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