Between classification-error approximation and weighted least-squares learning.
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.
Toh
Kar-Ann
KA
Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul, Korea. katoh@yonsei.ac.kr
Eng
How-Lung
HL
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016018:Least-Squares Analysis; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*