One-class document classification via Neural Networks
Neurocomputing
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Proximal support vector machine using local information
Neurocomputing
Manifold-based learning and synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A class of sparsely connected autoassociative morphological memories for large color images
IEEE Transactions on Neural Networks
k: nearest neighbors associative memory model for face recognition
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Nonlinear quantization on Hebbian-type associative memories
Applied Intelligence
Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Machine Vision and Applications
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Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional nonlinear autoassociation models emphasize searching for the nonlinear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.