DocumentCode
348660
Title
A quick and naive Euclidean learner for supervised feature selection
Author
Chan, Tony Y T
Author_Institution
Aizu Univ., Fukushima, Japan
Volume
1
fYear
1999
fDate
1999
Firstpage
587
Abstract
A model is proposed for learning to classify patterns under the Euclidean setting. Each pattern is represented by a vector in a fixed D-dimensional Euclidean space. Patterns are divided into training and test sets. Eleven experiments were performed. The proposed naive learner is found to be extremely fast and yet the correct classification rates are respectable even when compared with some of the best known rates
Keywords
feature extraction; learning (artificial intelligence); pattern classification; sequential estimation; vectors; classification rates; fixed D-dimensional Euclidean space; machine learning; pattern classification; quick naive Euclidean learner; sequential forward selection algorithm; supervised feature selection; test sets; training sets; vector representation; Euclidean distance; Extraterrestrial measurements; Fuzzy neural networks; Machine learning; Mathematical model; Neural networks; Probability; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on
Conference_Location
Pafos
Print_ISBN
0-7803-5682-9
Type
conf
DOI
10.1109/ICECS.1999.812353
Filename
812353
Link To Document