DocumentCode
504254
Title
Finding error data for linear separable model
Author
Ota, Yuki ; Tanaka, Masahiro
Author_Institution
Dept. of Inf. Sci. & Syst. Eng., Konan Univ., Kobe, Japan
fYear
2009
fDate
18-21 Aug. 2009
Firstpage
4437
Lastpage
4441
Abstract
In this paper, we consider determination of separating hyperplane where some data points are treated as noise. Pocket algorithm with ratchet is an algorithm for this kind of problems, but it does not guarantee minimal number of noisy instances. By noticing that a hyperplane can be determined by selecting n points among data (where n is the dimension of the data), we can guarantee that point. Actually, we found that the second and the third category of iris data is linearly separable by admitting only one instance of noisy data.
Keywords
data handling; multilayer perceptrons; noise; error data; linear separable model; multilayer perception; noisy data; pocket algorithm; separating hyperplane determination; Data engineering; Electronic mail; Informatics; Information science; Iris; Machine learning algorithms; Noise reduction; Systems engineering and theory; Training data; Working environment noise; Perceptron; linearly separable; noisy data;
fLanguage
English
Publisher
ieee
Conference_Titel
ICCAS-SICE, 2009
Conference_Location
Fukuoka
Print_ISBN
978-4-907764-34-0
Electronic_ISBN
978-4-907764-33-3
Type
conf
Filename
5332983
Link To Document