Polychotomous Regression
提出一种自动程序，使用线性样条及其张量积对多分类响应变量进行回归建模，可用于多分类问题，并通过最大似然估计、逐步回归和AIC等准则选择模型。
Abstract An automatic procedure that uses linear splines and their tensor products is proposed for fitting a regression model to data involving a polychotomous response variable and one or more predictors. The fitted model can be used for multiple classification. The automatic fitting procedure involves maximum likelihood estimation, stepwise addition, stepwise deletion, and model selection by the Akaike information criterion, cross-validation, or an independent test set. A modified version of the algorithm has been constructed that is applicable to large datasets, and it is illustrated using a phoneme recognition dataset with 250,000 cases, 45 classes, and 63 predictors.