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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 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.