Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FUZZY.2008.4630397 | FUZZ-IEEE |
Keywords | Field | DocType |
fuzzy set theory,prior knowledge representation,learning (artificial intelligence),regression analysis,knowledge representation,support vector machine,fuzzy kernel,learning theory,fuzzy if-then rule,support vector machines,fuzzy knowledge set,risk management,artificial neural networks,fuzzy sets,polynomials,training data,mathematical model,kernel,support vector regression,learning artificial intelligence | Data mining,Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy set,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Membership function,Machine learning | Conference |
Volume | Issue | ISSN |
null | null | 1098-7584 E-ISBN : 978-1-4244-1819-0 |
ISBN | Citations | PageRank |
978-1-4244-1819-0 | 1 | 0.35 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Wang | 1 | 8 | 1.51 |
| Zhi-chun Mu | 2 | 26 | 4.39 |
| Hui Guo | 3 | 1 | 0.35 |