Conference Proceeding

Prior knowledge-based fuzzy Support Vector Regression

Dept. of Autom., Univ. of Sci. & Technol. Beijing, Beijing
IEEE International Conference on Fuzzy Systems 07/2008; DOI:10.1109/FUZZY.2008.4630397 ISBN: 978-1-4244-1818-3 pp.392 - 395 In proceeding of: Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Source: IEEE Xplore

ABSTRACT A new method was proposed for incorporating prior knowledge in the form of fuzzy knowledge sets into Support Vector Machine for regression problem. The prior knowledge of Fuzzy IF-THEN rules can be transformed into fuzzy information to generate fuzzy kernel, based on which FSVR (Fuzzy Support Vector Regression) is introduced. The merit of FSVR is that it can incorporate with prior knowledge represented by fuzzy IF-THEN rules to improve the performance of the conventional SVR in incomplete numeral dataset for training. The simulation results are feasible.

0 0
 · 
0 Bookmarks
 · 
24 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

conventional SVR
 
fuzzy IF-THEN rules
 
fuzzy information
 
fuzzy kernel
 
fuzzy knowledge sets
 
Fuzzy Support Vector Regression
 
incomplete numeral dataset
 
incorporating prior knowledge
 
new method
 
prior knowledge
 
simulation results
 
Support Vector Machine
 

Ling Wang