Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653561 | Neurocomputing | 2005 | 20 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Alessio Micheli, Filippo Portera, Alessandro Sperduti,