Name
Title | ||
|---|---|---|
An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets. |
Abstract | ||
|---|---|---|
Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TNNLS.2014.2359954 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Support vector machines,Kernel,Time complexity,Vectors,Educational institutions,Linear programming,Learning systems | Kernel (linear algebra),Structured support vector machine,Least squares support vector machine,Pattern recognition,Matrix (mathematics),Computer science,Support vector machine,Artificial intelligence,Classifier (linguistics),Time complexity,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
26 | 9 | 2162-237X |
Citations | PageRank | References |
1 | 0.35 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingzhong Shi | 1 | 1 | 0.35 |
| Fu Lai Chung | 2 | 1534 | 86.72 |
| Shitong Wang | 3 | 1485 | 109.13 |