Name
Abstract | ||
|---|---|---|
•We propose a novel incremental and decremental variant of the TWSVM called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) for solving incremental learning problems.•The proposed approach can continuously integrate new information into already-built models and it is adherent to the structural risk minimization principle, and it uses the dual coordinate descent (DCD) algorithm with active shrinking to create the off-line classifier.•The incremental and decremental strategies are based on the DCD with shrinking, exploiting the relevance of each support vector.•We propose the use of our linear formulation with a kernel approximation to speed up training and classification while maintaining the non-linearity. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ins.2020.03.038 | Information Sciences |
Keywords | DocType | Volume |
Twin-SVM,Incremental learning,Multiclass twin-SVM,Data stream,On-line learning | Journal | 526 |
ISSN | Citations | PageRank |
0020-0255 | 2 | 0.36 |
References | Authors | |
32 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| de Mello Alexandre Reeberg | 1 | 2 | 0.36 |
| Marcelo Ricardo Stemmer | 2 | 13 | 3.80 |
| Alessandro L. Koerich | 3 | 525 | 39.59 |