Structural Risk Minimization for Character Recognition

The method of Structural Risk Minimization refers to tuning the capacity of the classifier to the available amount of training data. This capac(cid:173) ity is influenced by several factors, including: (1) properties of the input space, (2) nature and structure of the classifier, and (3) learning algorithm. Actions based on these three factors are combined here to control the ca(cid:173) pacity of linear classifiers and improve generalization on the problem of handwritten digit recognition. A common way of training a given classifier is to adjust the parameters w in the classification function F( x, w) to minimize the training error Etrain, i.e. the fre(cid:173) quency of errors on a set of p training examples. Etrain estimates the expected risk based on the empirical data provided by the p available examples.