Name
Abstract | ||
|---|---|---|
The increasing complexity of modern robots makes it prohibitively hard to accurately model such systems as required by many applications. In such cases, machine learning methods offer a promising alternative for approximating such models using measured data. To date, high computational demands have largely restricted machine learning techniques to mostly offline applications. However, making the robots adaptive to changes in the dynamics and to cope with unexplored areas of the state space requires online learning. In this paper, we propose an approximation of the support vector regression (SVR) by sparsification based on the linear independency of training data. As a result, we obtain a method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques, such as v-SVR, Gaussian process regression (GPR) and locally weighted projection regression (LWPR). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IROS.2009.5354609 | IROS |
Keywords | DocType | ISBN |
weighted projection regression,sparse online model,restricted machine,robots,learning (artificial intelligence),support vector regression,regression analysis,real-time online learning,high computational demand,sparse online model learning,online learning,gaussian process regression,locally weighted projection regression,robot control,competitive learning accuracy,machine learning,standard regression technique,support vector machines,training data,sparsification,dictionaries,linear independence,competitive learning,data models,inverse problems,brain computer interfaces,real time systems,computational modeling,real time,learning artificial intelligence,state space | Conference | 978-1-4244-3804-4 |
Citations | PageRank | References |
8 | 0.60 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| duy nguyentuong | 1 | 438 | 26.22 |
| Bernhard Schölkopf | 2 | 23120 | 3091.82 |
| Jan Peters | 3 | 3553 | 264.28 |