Quantum optimization for training support vector machines

Authors:
Davide Anguita;Sandro Ridella;Fabio Rivieccio;Rodolfo Zunino
Affiliations:
DIBE--Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11A 16145 Genova, Italy;DIBE--Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11A 16145 Genova, Italy;DIBE--Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11A 16145 Genova, Italy;DIBE--Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11A 16145 Genova, Italy
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

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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 On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques.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.