@inproceedings{9d62a260c7d64019821eeee1765554f3,
title = "Regularization for regression models based on the k-functional with Besov norm",
abstract = "This paper presents a new method of regularization in regression problems using a Besov norm (or semi-norm) acting as a regularization operator. This norm is more general smoothness measure to approximation spaces compared to other norms such as Sobolev and RKHS norms which are usually used in the conventional regularization methods. In our work, we also suggest a new candidate of the regularization parameter, that is, the trade-off between the data fit and the smoothness of the estimation function. Through the simulation for function approximation, we have shown that the suggested regularization method is effective and the estimated values of regularization parameters are close to the optimal values associated with the minimum expected risks.",
author = "Imhoi Koo and Kil, \{Rhee Man\}",
year = "2007",
month = jan,
day = "1",
doi = "10.1007/978-3-540-72383-7\_131",
language = "English (US)",
isbn = "9783540723820",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "1117--1126",
booktitle = "Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings",
address = "Germany",
edition = "PART 1",
note = "4th International Symposium on Neural Networks, ISNN 2007 ; Conference date: 03-06-2007 Through 07-06-2007",
}