Name
Abstract | ||
|---|---|---|
A key problem of memory based learning methods is the selection of a good smoothing or bandwidth parameter that defines the region over which generalization is performed. In this article we present a novel algorithm to answer this question by utilizing the information from confidence intervals, to compute a bandwidth. The basic idea is the usage of confidence intervals to get a statistical statement about the quality of fit between estimated model and process. As long as the prediction intervals of a certain model include the neighboring data points of an incremental growing validity region, it is considered to be a good fit. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ACC.2011.5991139 | American Control Conference |
Keywords | Field | DocType |
learning (artificial intelligence),pattern classification,statistical analysis,bandwidth parameter,incremental growing validity region,lazy learning,memory based learning methods,neighboring data points,reliable nearest neighbors,smoothing parameter,statistical statement,mathematical model,noise,kernel,prediction algorithms,prediction model,data models,computational modeling,computer model,data model,nearest neighbor,learning artificial intelligence,predictive models | Kernel (linear algebra),Data point,Data mining,Data modeling,Computer science,Lazy learning,Smoothing,Prediction interval,Bandwidth (signal processing),Confidence interval | Conference |
ISSN | ISBN | Citations |
0743-1619 | 978-1-4577-0080-4 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Ebert | 1 | 0 | 0.68 |
| Kampmann, G. | 2 | 0 | 0.34 |
| Oliver Nelles | 3 | 99 | 17.27 |