Abstract
We discuss an analytic model selection for k-nearest neighbors regression method using VC generalization bounds. Whereas existing implementations of k-nn regression estimate the model complexity as n/k, where n is the number of samples, we propose a new model complexity estimate. The proposed new complexity index used as the VC-dimension in VC bounds yields a new analytic method for model selection. Empirical results for low dimensional and high dimensional data sets indicate that the proposed model selection approach provides accurate model selection that is consistently better than previously used complexity measure. In fact, prediction accuracy of the proposed analytic method is similar to resampling (cross-validation) approach for optimal selection of k.
Original language | English (US) |
---|---|
Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
Pages | 1143-1148 |
Number of pages | 6 |
Volume | 2 |
State | Published - Sep 24 2003 |
Event | International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: Jul 20 2003 → Jul 24 2003 |
Other
Other | International Joint Conference on Neural Networks 2003 |
---|---|
Country/Territory | United States |
City | Portland, OR |
Period | 7/20/03 → 7/24/03 |