Kernels for structured domains are widely adopted in real-world applications that involve learning on structured data. In this context many kernels have been proposed in literature, but no theoretical comparison among them is present. In this paper we provide different formal definitions of expressiveness of a kernel by exploiting the most recent results in the field of Statistical Learning Theory, and analyze the differences among some state-of-the-art graph kernels. Results on real world datasets confirm some known properties of graph kernels, showing that Statistical Learning Theory is indeed a powerful and practical tool able to perform this analysis.

Measuring the expressivity of graph kernels through Statistical Learning Theory

Oneto, Luca;Anguita, Davide
2017

Abstract

Kernels for structured domains are widely adopted in real-world applications that involve learning on structured data. In this context many kernels have been proposed in literature, but no theoretical comparison among them is present. In this paper we provide different formal definitions of expressiveness of a kernel by exploiting the most recent results in the field of Statistical Learning Theory, and analyze the differences among some state-of-the-art graph kernels. Results on real world datasets confirm some known properties of graph kernels, showing that Statistical Learning Theory is indeed a powerful and practical tool able to perform this analysis.
File in questo prodotto:
File Dimensione Formato  
J024 - NEUCOM.pdf

accesso chiuso

Tipologia: Documento in versione editoriale
Dimensione 1.79 MB
Formato Adobe PDF
1.79 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

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: http://hdl.handle.net/11567/881455
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 7
social impact