DocumentCode
1657745
Title
Unsupervised classification for the triple parity strings
Author
Chan, Tony Y T
Author_Institution
Univ. of Aizu, Fukushima, Japan
Volume
2
fYear
2001
fDate
6/23/1905 12:00:00 AM
Firstpage
615
Abstract
A method is proposed for supervised and unsupervised learning to classify bit strings for three classes. The learner was modeled by two formal concepts, transformation system and stability optimization. Even though a small set of short examples were used in the training stage, all bit strings of any length were classified correctly in the online recognition stage. The learner successfully learned to devise a way by means of metric calculations to classify bit strings according to 3-parity-ness, while the learner was never told the concept of 3-parity-ness
Keywords
learning (artificial intelligence); pattern classification; unsupervised learning; 3-parity-ness; bit string classification; formal concepts; metric calculations; online recognition stage; pattern classification; stability optimization; supervised learning; transformation system; triple parity strings; unsupervised learning; Biological cells; Costs; Extraterrestrial measurements; Machine learning; Scattering; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronics, Circuits and Systems, 2001. ICECS 2001. The 8th IEEE International Conference on
Print_ISBN
0-7803-7057-0
Type
conf
DOI
10.1109/ICECS.2001.957551
Filename
957551
Link To Document