DocumentCode
2197116
Title
Fast Density Estimation for Approximated k Nearest Neighbor Classification
Author
Kobayashi, Takao ; Shimizu, Ikuko
Author_Institution
Dept. of Comput. & Inf. Sci., Tokyo Univ. of Agric. & Technol., Koganei, Japan
fYear
2010
fDate
16-18 Nov. 2010
Firstpage
345
Lastpage
351
Abstract
We propose a method for fast density estimation of samples, which makes it possible to significantly accelerate classification based on the k nearest neighbor (kNN) method. Our main premise is that many trials of a rough estimation of probability density function are conducted, and they are integrated by Bayes´ theorem. The experimental results indicated that the classification time used in our method was at least 30 times faster than that of kNN.
Keywords
approximation theory; pattern classification; approximated k nearest neighbor classification; fast density estimation; probability density function; bayes theorem; k nearest neighbor method; locality sensitive hashing; partition of a space;
fLanguage
English
Publisher
ieee
Conference_Titel
Frontiers in Handwriting Recognition (ICFHR), 2010 International Conference on
Conference_Location
Kolkata
Print_ISBN
978-1-4244-8353-2
Type
conf
DOI
10.1109/ICFHR.2010.60
Filename
5693547
Link To Document