view in publisher's site

Robust Novelty Detection via Worst Case CVaR Minimization

Novelty detection models aim to find the minimum volume set covering a given probability mass. This paper proposes a robust single-class support vector machine (SSVM) for novelty detection, which is mainly based on the worst case conditional value-at-risk minimization. By assuming that every input is subject to an uncertainty with a specified symmetric support, this robust formulation results in a maximization term that is similar to the regularization term in the classical SSVM. When the uncertainty set is ℓ 1 -norm, ℓ -norm or box, its training can be reformulated to a linear program; while the uncertainty set is ℓ 2 -norm or ellipsoidal, its training is a tractable secondorder cone program. The proposed method has a nice consistent statistical property. As the training size goes to infinity, the estimated normal region converges to the true provided that the magnitude of the uncertainty set decreases in a systematic way. The experimental results on three data sets clearly demonstrate its superiority over three benchmark models.

کشف تازه کشف تازه از طریق بدترین حالت cvar Minimization

ترجمه شده با

سفارش ترجمه مقاله و کتاب - شروع کنید

95/12/18 - با استفاده از افزونه دانلود فایرفاکس و کروم٬ چکیده مقالات به صورت خودکار تشخیص داده شده و دکمه دانلود فری‌پیپر در صفحه چکیده نمایش داده می شود.