Name
Abstract | ||
|---|---|---|
Support vector machines are widely used for classification and regression tasks. They provide reliable static models, but their extension to the training of dynamic models is still an open problem. In the present paper, we describe Regularized Recurrent Support Vector Machines, which, in contrast to previous Recurrent Support Vector Machine, models, allow the design of dynamical models while retaining the built-in regularization mechanism present in Support Vector Machines. The principle is validated on academic examples, it is shown that the results compare favorably to those obtained by unregularized Recurrent Support Vector Machines and to regularized, partially recurrent Support Vector Machines. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCBS.2009.58 | IJCBS |
Keywords | Field | DocType |
computational modeling,dynamic system,support vector machines,artificial neural networks,support vector machine,noise,dynamic systems,machine learning,mathematical model,least squares support vector machine,modeling,predictive models | Least squares,Pattern recognition,Least squares support vector machine,Computer science,Support vector machine,Regularization (mathematics),Artificial intelligence,Relevance vector machine,Margin classifier,Artificial neural network,Machine learning,Regularization perspectives on support vector machines | Conference |
Volume | Issue | Citations |
null | null | 7 |
PageRank | References | Authors |
0.72 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haini Qu | 1 | 31 | 2.03 |
| Y. Oussar | 2 | 294 | 26.32 |
| Gérard Dreyfus | 3 | 475 | 58.97 |
| Weisheng Xu | 4 | 65 | 8.28 |