Incremental face recognition for large-scale social network services
Pattern Recognition
An online learning network for biometric scores fusion
Neurocomputing
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
Hi-index | 0.14 |
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.