Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Hidden Tree Markov Models for Document Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.