One-class SVM regularization path and comparison with alpha seeding

One-class support vector machines (1-SVMs) estimate the level set of the underlying density observed data. Aside the kernel selection issue, one difficulty concerns the choice of the ’level’ parameter. In this paper, following the work by Hastie et. al (2004), we derive the entire regularization path for ν-1-SVMs. Since this regularization path is efficient for building different level sets estimate, we have empirically compared such approach to state of the art approach based on alpha seeding and we show that regularization path is far more efficient.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Alain Rakotomamonjy, Manuel Davy
• Keywords: Entire Regularization Path, ESANN 2007, Level Sets, Neural Networks, Regularization Path