Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized discrepancy and quadrature error bound
Mathematics of Computation
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Adaptive Regularization in Neural Network Modeling
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Objective Optimization for SVM Model Selection
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
International Journal of Knowledge-based and Intelligent Engineering Systems
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
Gabor features-based classification using SVM for face recognition
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A sub-block-based eigenphases algorithm with optimum sub-block size
Knowledge-Based Systems
Hi-index | 12.05 |
The primary difficulty of support vector machine (SVM) model selection is heavy computational cost, thus it is difficult for current model selection methods to be applied in face recognition. Model selection via uniform design can effectively alleviate the computational cost, but its drawback is that it adopts a single objective criterion which can not always guarantee the generalization capacity. The sensitivity and specificity as multi-objective criteria have been proved of better performance and can provide a means for obtaining more realistic models. This paper first proposes a multi-objective uniform design (MOUD) search method as a SVM model selection tool, and then applies this optimized SVM classifier to face recognition. Because of replacing single objective criterion with multi-objective criteria and adopting uniform design to seek experimental points that uniformly scatter on whole experimental domain, MOUD can reduce the computational cost and improve the classification ability simultaneously. The experiments are executed on UCI benchmark, and on Yale and CAS-PEAL-R1 face databases. The experimental results show that the proposed method outperforms other model search methods significantly, especially for face recognition.