Perceptron models for online structured prediction

Abstract

Our structured prediction problem is formulated as a convex optimization problem of maximal margin [5-6], quite similar to the formulation of multiclass support vector machines (MSVM) [8]. It is applied to predict costs among states of paths. Predicting them properly is very important, because the problem of paths planning depends on its correctness. Ratliff [4] showed a maximum margin approach which allows the prediction of costs in different environments using subgradient method. As a contribution of this work, we developed new solution methods: the first one, called Structured Perceptron, has similarities with the correction scheme proposed by [1] and the second one is called Structured IMA. It is derived from the work presented by [2]. Both use the Perceptron model. The proposed algorithms were more efficient in terms of computational effort and similar in prediction quality when compared with [4]. © 2012 Springer-Verlag.