DocumentCode
1749236
Title
Performance characterization of K-winner machines
Author
Ridella, Sandro ; Zunino, Rodolfo
Author_Institution
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Volume
2
fYear
2001
fDate
2001
Firstpage
1227
Abstract
The paper reports on new findings about the properties of K-winner machines (KWMs). The resulting theoretical model is sharply characterized in terms of generalization performance, and exhibits interesting features from an application perspective as well. The major novel aspect lies in connecting analytically the KWM framework to established methods, proposed by Vapnik and Cherkassky, for assessing a classifier´s generalization performance
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); neural nets; pattern classification; vector quantisation; K-winner machines; VC dimension; Vapnik expression; generalization; learning; pattern classification; performance; vector quantisation; Algorithm design and analysis; Calibration; Design optimization; Error analysis; Joining processes; Optical wavelength conversion; Performance analysis; Prototypes; Testing; Yield estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939536
Filename
939536
Link To Document