The nature of statistical learning theory
The nature of statistical learning theory
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Incremental margin algorithm for large margin classifiers
Neurocomputing
Distributed training strategies for the structured perceptron
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
| Hi-index | 0.00 |
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].