Protein fold recognition with adaptive local hyperplane algorithm
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Adaptive local hyperplane for regression tasks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Margin-based ensemble classifier for protein fold recognition
Expert Systems with Applications: An International Journal
Survey of various feature extraction and classification techniques for facial expression recognition
EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
Adaptive weighted fusion of local kernel classifiers for effective pattern classification
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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The paper introduces a novel adaptive local hyperplane (ALH) classifier and it shows its superior performance in the face recognition tasks. Four different feature extraction methods (2DPCA, (2D)2PCA, 2DLDA and (2D)2LDA) have been used in combination with five classifiers (K-nearest neighbor (KNN), support vector machine (SVM), nearest feature line (NFL), nearest neighbor line (NNL) and ALH). All the classifiers and feature extraction methods have been applied to the renown benchmarking face databases—the Cambridge ORL database and the Yale database and the ALH classifier with a LDA based extractor outperforms all the other methods on them. The ALH algorithm on these two databases is very promising but more study on larger databases need yet to be done to show all the advantages of the proposed algorithm.