A Preliminary Empirical Comparison of Recursive Neural Networks and Tree kernel Methods on Regression Tasks for Tree Structured Domains

he 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.