The definition of appropriate kernel functions is crucial for the performance of a kernel method. In many of the state-of-the-art kernels for trees, matching substructures are considered independently from their position within the trees. However, when a match happens in similar positions, more strength could reasonably be given to it. Here, we give a systematic way to enrich a large class of tree kernels with this kind of information without affecting, in almost all cases, the worst case computational complexity. Experimental results show the effectiveness of the proposed approach.

Extending Tree Kernels with Topological Information

AIOLLI, FABIO;DA SAN MARTINO, GIOVANNI;SPERDUTI, ALESSANDRO
2011

Abstract

The definition of appropriate kernel functions is crucial for the performance of a kernel method. In many of the state-of-the-art kernels for trees, matching substructures are considered independently from their position within the trees. However, when a match happens in similar positions, more strength could reasonably be given to it. Here, we give a systematic way to enrich a large class of tree kernels with this kind of information without affecting, in almost all cases, the worst case computational complexity. Experimental results show the effectiveness of the proposed approach.
2011
Proceedings of the 21st International Conference on Artificial Neural Networks, ICANN 2011
21st International Conference on Artificial Neural Networks - ICANN 2011
9783642217340
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2440692
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 6
  • OpenAlex ND
social impact