  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 data 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.

Methodology;Machine Learning