Ho–Kashyap classifier with early stopping for regularization

This paper focuses on linear classification using a fast and simple algorithm known as the Ho–Kashyap learning rule (HK). In order to avoid overfitting and instead of adding a regularization parameter in the criterion, early stopping is introduced as a regularization method for HK learning, which becomes HKES (Ho–Kashyap with early stopping). Furthermore, an automatic procedure, based on the generalization error estimation, is proposed to tune the stopping time. The method is then tested and compared to others (including SVM and LSVM), that use either ℓ1 or ℓ2-norm of the errors, on well-known benchmarks. The results show the limits of early stopping for regularization with respect to the generalization error estimation and the drawbacks of low level hyperparameters such as a number of iterations.