Multi-class classification methods based on both labeled and unlabeled
functional data sets are discussed. We present semi-supervised logistic models
for classification in the context of functional data analysis. Unknown
parameters in our proposed models are estimated by regularization with the help
of EM algorithm. Crucial points in modeling procedure are the choices of
regularization parameter involved in the semi-supervised functional logistic
models. In order to select the adjusted parameter, we introduce model selection
criteria from information-theoretic and Bayesian viewpoints. Monte Carlo
simulations and real data analysis are given to examine the effectiveness of
proposed modeling strategies.