Title :
Foundations of Immunocomputing
Author :
Tarakanov, Alexander ; Nicosia, Giuseppe
Author_Institution :
St. Petersburg Inst. for Informatics & Autom., Russian Acad. of Sci., St. Petersburg
Abstract :
This paper presents the mathematical basis of the immunocomputing using feature extraction and pattern recognition. The key notions of the approach are the formal immune network (FIN) and the coding theory for machine learning. The training of FIN includes apoptosis (programmed cell death) and auto immunization both controlled by cytokines (messenger proteins), whereas parameters of FIN can be optimized by Kullback entropy. Recent results suggest that the approach outperforms (by training time and accuracy) state-of-art approaches of computational intelligence
Keywords :
artificial immune systems; learning (artificial intelligence); pattern recognition; Kullback entropy; autoimmunization; coding theory; feature extraction; formal immune network; immunocomputing; machine learning; messenger proteins; pattern recognition; programmed cell death; Application specific integrated circuits; Artificial neural networks; Biology computing; Biomedical signal processing; Computational intelligence; Computer networks; Feature extraction; Immune system; Pattern recognition; Proteins;
Conference_Titel :
Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0703-6
DOI :
10.1109/FOCI.2007.371519
