Evidence Maximization Technique for Training of Elastic Nets

Abstract EN

This paper presents a technique of evidence maximization for automatic tuning of regularization parameters of elastic nets, which allows tuning many parameters simultaneously.

This technique was applied to handwritten digit recognition.

Experiments showed its ability to train either models with high accuracy of recognition or highly sparse models with reasonable accuracy.