A preliminary empirical comparison of recursive neural networks and tree kernel methods on regression tasks for tree structured domains

The aim of this paper is to start a comparison between Recursive Neural Networks (RecNN) and kernel methods for structured data, specifically Support Vector Regression (SVR) machine using a Tree Kernel, in the context of regression tasks for trees. Both the approaches can deal directly with a structured input representation and differ in the construction of the feature space from structured data. We present and discuss preliminary empirical results for specific regression tasks involving well-known Quantitative Structure-Activity and Quantitative Structure-Property Relationship (QSAR/QSPR) problems, where both the approaches are able to achieve state-of-the-art results.