Title :
Unsupervised classification for the triple parity strings
Author_Institution :
Univ. of Aizu, Fukushima, Japan
fDate :
6/23/1905 12:00:00 AM
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;
Conference_Titel :
Electronics, Circuits and Systems, 2001. ICECS 2001. The 8th IEEE International Conference on
Print_ISBN :
0-7803-7057-0
DOI :
10.1109/ICECS.2001.957551
