Optimizing resources in model selection for support vector machine

Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the bounds of the leave-one-out (LOO) such as radius-margin bound and on the performance measures such as Generalized Approximate Cross Validation (GACV), empirical error, etc. These usual automatic methods used to tune the hyperparameters require an inversion of the Gram-Schmidt matrix or a resolution of an extra-quadratic programming problem. In the case of a large dataset these methods require the addition of huge amounts of memory and a long CPU time to the already significant resources used in SVM training. In this paper, we propose a fast method based on an approximation of the gradient of the empirical error, along with incremental learning, which reduces the resources required both in terms of processing time and of storage space. We tested our method on several benchmarks, which produced promising resu...

• Type: Journal
• Year: 2007
• Where: PR
• Authors: Mathias M. Adankon, Mohamed Cheriet
• Keywords: Approximate Cross Validation, Empirical Error, PR 2007, Usual Automatic Methods