  We develop a mixtures-of-experts (ME) approach to the multiclass
classification where the predictors are univariate functions. It consists of a
ME model in which both the gating network and the experts network are
constructed upon multinomial logistic activation functions with functional
inputs. We perform a regularized maximum likelihood estimation in which the
coefficient functions enjoy interpretable sparsity constraints on targeted
derivatives. We develop an EM-Lasso like algorithm to compute the regularized
MLE and evaluate the proposed approach on simulated and real data.

Machine Learning;Artificial Intelligence;Machine Learning