title: Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System.
abstract: In this paper, a fast kernel ridge regression (KRR) learning algorithm is adopted with ( ) training cost for large-scale active authentication system. A truncated Gaussian radial basis function (TRBF) kernel is also implemented to provide better cost-performance tradeoff. The fast-KRR algorithm along with the TRBF kernel offers computational advantages over the traditional support vector machine (SVM) with Gaussian-RBF kernel while preserving the error rate performance. Experimental results validate the cost-effectiveness of the developed authentication system. In numbers, the fast-KRR learning model achieves an equal error rate (EER) of 1.39% with ( ) training time, while SVM with the RBF kernel shows an EER of 1.41% with ( ) training time.
authors.forename: Pei-Yuan
authors.initials: PY
authors.lastname: Wu
authors.forename: Chi-Chen
authors.initials: CC
authors.lastname: Fang
authors.forename: Jien Morris
authors.initials: JM
authors.lastname: Chang
authors.forename: Sun-Yuan
authors.initials: SY
authors.lastname: Kung