Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Experts Organization Model for Natural Scene Images Classification
Neural Processing Letters
Biometric scores fusion based on total error rate minimization
Pattern Recognition
A local experts organization model with application to face emotion recognition
Expert Systems with Applications: An International Journal
Multimodal biometrics: state of the art in fusion techniques
International Journal of Biometrics
Nonlinear controller optimization of a power system based on reduced multivariate polynomial model
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A boundary based classifier combination method
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Improving fusion with margin-derived confidence in biometric authentication tasks
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Some issues pertaining to adaptive multimodal biometric authentication
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Relaxation of hard classification targets for LSE minimization
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Reduced analytical dependency modeling for classifier fusion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Hi-index | 0.00 |
The multivariate polynomial model provides an effective way to describe complex nonlinear input-output relationships since it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high-dimensional and high-order problems, multivariate polynomial regression becomes impractical due to its huge number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomial model to circumvent the dimensionality problem with some compromise in its approximation capability. In multimodal biometrics and many classifiers fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, this reduced multivariate polynomial model can be used to combine these classifiers in the next level of classification taking their outputs as the inputs to the reduced multivariate polynomial model. The model is first applied to a well-known pattern classification problem to illustrate its classification capability. The reduced multivariate polynomial model is then applied to combine two biometric verification systems with improved receiver operating characteristics performance as compared to an optimal weighing method and a few commonly used classifiers.