Abstract. One approach to improve the accuracy of classifications based on generative models is to combine them with successful discriminative algorithms. Fisher kernels were developed to combine generative models with a currently very popular class of learning algorithms, kernel methods. Empirically, the combination of hidden Markov models with support vector machines has shown promising results. So far, however, Fisher kernels have only been considered for sequences over flat alphabets. This is mostly due to the lack of a method for computing the gradient of a generative model over structured sequences. In this paper, we show how to compute the gradient of logical hidden Markov models, which allow for the modelling of logical sequences, i.e., sequences over an alphabet of logical atoms. Experiments show a considerable improvement over results achieved without Fisher kernels for logical sequences.
In REF has been proposed an extension of classical Fisher kernels, working on sequences over flat alphabets, in order to make them able to model logical sequences, i.e., sequences over an alphabet of logical atoms.
Fisher kernels for logical sequences
In Proc. of 15th European Conference on Machine Learning (ECML-04
Computer Science Mathematics