The problem of serial order: a neural network model of sequence learning and recall
Current research in natural language generation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Network Realization of Support Vector Methods for Pattern Classification
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Learning with non-positive kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Neural Computation
A neural support vector machine
Neural Networks
A one-layer recurrent neural network for support vector machine learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improved neural network for SVM learning
IEEE Transactions on Neural Networks
Analog neural network for support vector machine learning
IEEE Transactions on Neural Networks
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Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and @n-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full @n-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme.