Name
Abstract | ||
|---|---|---|
When a linear regression model is constructed by statistical calculation, all the data are treated without order, even if they are order data. We propose the Geometric regression and geometric relation method (GR2) to utilize the relation information inside the order of data. The GR2 transforms the order data of each variable to a curve (or geometric relation), and uses the curves to establish a geometric regression model. The prediction method using this geometric regression model is developed to give predictions. Experimental results on simulated and real datasets show that the GR2 method is effective and has lower prediction errors than traditional linear regression. © 2012 Springer-Verlag. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31588-6_31 | ICIC (1) |
Keywords | Field | DocType |
geometric regression,geometric relation,relations between data | Geometric data analysis,Regression,Regression analysis,Polynomial regression,Proper linear model,Bayesian multivariate linear regression,Artificial intelligence,Statistics,Mathematics,Machine learning,Segmented regression,Linear regression | Conference |
Volume | Issue | ISSN |
7389 LNCS | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaijun Wang | 1 | 70 | 4.86 |
| Liying Yang | 2 | 11 | 7.05 |