Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Letters: Convex incremental extreme learning machine
Neurocomputing
Journal of Cognitive Neuroscience
Face recognition based on multi-class SVM
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition
IEEE Transactions on Information Forensics and Security
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
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The original extreme learning machine (ELM), based on least square solutions, is an efficient learning algorithm used in "generalized" single-hidden layer feedforward networks (SLFNs) which need not be neuron alike. Latest development[1] shows that ELM can be implemented with kernels. Kernlized ELM can be seen as a variant of the conventional LS-SVM without the output bias b. In this paper, the performance comparison of LS-SVM and kernelized ELM is conducted over a benchmarking face recognition dataset. Simulation results show that the kernelized ELM outperforms LS-SVM in terms of both recognition prediction accuracy and training speed.