Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System
该文采用快速核岭回归算法和截断高斯径向基核函数，在击键动态身份认证系统中以更低训练成本实现与支持向量机相当的等错误率（1.39% vs 1.41%），适合关注高效认证算法的研究者。
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.