| Creator |
|
|
|
| Language |
|
| Publisher |
|
|
|
| Date |
|
| Source Title |
|
| Vol |
|
| First Page |
|
| Last Page |
|
| Publication Type |
|
| Access Rights |
|
| Crossref DOI |
|
| Related DOI |
|
| Related URI |
|
| Relation |
|
| Abstract |
Multi-class classification methods based on both labeled and unlabeled functional data sets are discussed. We present a semi-supervised logistic model for classification in the context of functional d...ata analysis. Unknown parameters in our proposed model are estimated by regularization with the help of EM algorithm. A crucial point in the modeling procedure is the choice of a regularization parameter involved in the semi-supervised functional logistic model. In order to select the adjusted parameter, we introduce model selection criteria from information-theoretic and Bayesian viewpoints. Monte Carlo simulations and a real data analysis are given to examine the effectiveness of our proposed modeling strategy.show more
|
Export Mendeley
KawanoKonishi_Final