Fisher kernel for tree structured data

We introduce a kernel for structured data, which is an extension of the Fisher kernel used for sequences. In our approach, we extract the Fisher score vectors from a Bayesian network, specifically a hidden tree Markov model, which can be constructed starting from the training data. Experiments on a QSPR (quantitative structure-property relationship) analysis, where instances are naturally represented as trees, allow a first test of the approach.