A Unified Framework for Regularization Networks and Support Vector Machines
Theodoros Evgeniou, Massimiliano Pontil, Tomaso Poggio · DSpace@MIT (Massachusetts Institute of Technology) · 1999
Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.