The well-known bounds on the generalizationability of learning machines, based on the Vapnik–Chernovenkis (VC) dimension,are very loose when applied to Support Vector Machines (SVMs).In this work we evaluate the validity of the assumption that these bounds are,nevertheless, good indicators of the generalization ability of SVMs.We show that this assumption is, in general, true and assess its correctness, in a statistical sense, on several pattern recognition benchmarks through the use of the bootstrap technique.
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap
ANGUITA, DAVIDE;RIDELLA, SANDRO
2000-01-01
Abstract
The well-known bounds on the generalizationability of learning machines, based on the Vapnik–Chernovenkis (VC) dimension,are very loose when applied to Support Vector Machines (SVMs).In this work we evaluate the validity of the assumption that these bounds are,nevertheless, good indicators of the generalization ability of SVMs.We show that this assumption is, in general, true and assess its correctness, in a statistical sense, on several pattern recognition benchmarks through the use of the bootstrap technique.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
