Quantum optimization for training support vector machines.
Neural Netw
; 16(5-6): 763-70, 2003.
Article
em En
| MEDLINE
| ID: mdl-12850032
ABSTRACT
Refined concepts, such as Rademacher estimates of model complexity and nonlinear criteria for weighting empirical classification errors, represent recent and promising approaches to characterize the generalization ability of Support Vector Machines (SVMs). The advantages of those techniques lie in both improving the SVM representation ability and yielding tighter generalization bounds. On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques. The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations. The presented research compares the behavioral aspects of conventional and enhanced SVMs; experiments in both a synthetic and real-world problems support the theoretical analysis. At the same time, the related differences between Quadratic-Programming and Quantum-based optimization techniques are considered.
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Base de dados:
MEDLINE
Assunto principal:
Teoria Quântica
/
Metodologias Computacionais
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2003
Tipo de documento:
Article