Title :
A quick and naive Euclidean learner for supervised feature selection
Author_Institution :
Aizu Univ., Fukushima, Japan
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;
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
DOI :
10.1109/ICECS.1999.812353
