Title :
Nonparametric classifier design using vector quantization
Author :
Xie, Qiaobing ; Ward, Rabab K. ; Laszlo, Charles A.
Author_Institution :
Dept. of Electr. Eng., British Columbia Univ., Vancouver, BC, Canada
Abstract :
VQ-based method is developed as an effective data reduction technique for nonparametric classifier design. This new technique, while insisting on competitive classification accuracy, is found to overcome the usual disadvantage of traditional nonparametric classifiers of being computationally complex and of requiring large amounts of computer storage
Keywords :
computational complexity; data reduction; vector quantisation; competitive classification accuracy; computational complexity; computer storage; data reduction; nonparametric classifier design; vector quantization; Algorithm design and analysis; Bayesian methods; Density functional theory; Density measurement; Design methodology; Error analysis; Kernel; Probability density function; Speech; Vector quantization;
Conference_Titel :
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location :
Alexandria, VA
Print_ISBN :
0-7803-2761-6
DOI :
10.1109/WITS.1994.513862
