He is now at the University of California, Berkeley.

He is now at the University of California, Berkeley.

Learning is posed as a problem of function estimation, for which two principles ofsolution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different statements ofthe function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition.

The method of Structural Risk Minimization refers to tuning the capacity of the classifier to the available amount of training data.

The method of Structural Risk Minimization refers to tuning the capacity of the classifier to the available amount of training data. This capacity 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 capacity of linear classifiers and improve generalization on the problem of handwritten digit recognition.

Learning is posed as a problem of function estimation, for which two principles of solution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different statements of the function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition.