Title :
Reducing bias in supervised learning
Author :
Gupta, Maya R. ; Gray, Robert M.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
fDate :
28 Sept.-1 Oct. 2003
Abstract :
Nonparametric statistical supervised learning methods often suffer from bias caused by non-uniformity of the probability distribution of training samples. This problem is discussed in this paper and a new nonparametric neighborhood method for classification and estimation that significantly reduces the bias is proposed. Simulations exemplify the advantages, and theoretical results are noted.
Keywords :
learning (artificial intelligence); nonparametric statistics; pattern classification; statistical distributions; bias reduction; nonparametric statistical methods; probability distribution nonuniformity; supervised learning; training samples; Engineering management; Error analysis; Frequency estimation; Kernel; Pattern recognition; Probability distribution; Supervised learning; Testing; Training data; Vectors;
Conference_Titel :
Statistical Signal Processing, 2003 IEEE Workshop on
Print_ISBN :
0-7803-7997-7
DOI :
10.1109/SSP.2003.1289452
