Toh Kar-Ann, Eng How-Lung
Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul, Korea.
IEEE Trans Pattern Anal Mach Intell. 2008 Apr;30(4):658-69. doi: 10.1109/TPAMI.2007.70730.
This paper presents a deterministic solution to an approximated classification-error based objective function. In the formulation, we propose a quadratic approximation as the function for achieving smooth error counting. The solution is subsequently found to be related to the weighted least-squares whereby a robust tuning process can be incorporated. The tuning traverses between the least-squares estimate and the approximated total-error-rate estimate to cater for various situations of unbalanced attribute distributions. By adopting a linear parametric classifier model, the proposed classification-error based learning formulation is empirically shown to be superior to that using the original least-squares-error cost function. Finally, it will be seen that the performance of the proposed formulation is comparable to other classification-error based and state-of-the-art classifiers without sacrificing the computational simplicity.
本文提出了一种针对基于近似分类误差的目标函数的确定性解决方案。在该公式中，我们提出了一种二次近似作为实现平滑误差计数的函数。随后发现该解决方案与加权最小二乘法相关，由此可以纳入一个稳健的调优过程。该调优在最小二乘估计和近似总错误率估计之间进行，以适应属性分布不平衡的各种情况。通过采用线性参数分类器模型，基于分类误差的学习公式在经验上被证明优于使用原始最小二乘误差代价函数的公式。最后，可以看到所提出公式的性能与其他基于分类误差的和最先进的分类器相当，同时又不牺牲计算的简便性。
