Synthesis of Multivalued Multithreshold Functions for CCD Implementation
IEEE Transactions on Computers
On the Optimal Design of Multiple-Valued PLAs
IEEE Transactions on Computers
Learning with discrete multi-valued neurons
Proceedings of the seventh international conference (1990) on Machine learning
Small depth polynomial size neural networks
Neural Computation
Advances in neural information processing systems 2
Computing with discrete multi-valued neurons
Journal of Computer and System Sciences
Learning with discrete multivalued neurons
Journal of Computer and System Sciences
Discrete neural computation: a theoretical foundation
Discrete neural computation: a theoretical foundation
Multi-valued multi-threshold networks
MVL '76 Proceedings of the sixth international symposium on Multiple-valued logic
Learning Multiple-Valued Logic Networks Based on Back Propagation
ISMVL '95 Proceedings of the 25th International Symposium on Multiple-Valued Logic
Synthesis of multiple-valued logic functions by neural networks
Synthesis of multiple-valued logic functions by neural networks
The Computing Capacity of Three-Input Multiple-Valued One-Threshold Perceptrons
Neural Processing Letters
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The (n,k,s)-perceptrons partition the input space V \subset Rn into s+1 regions using s parallel hyperplanes. Their learning abilities are examined in this research paper. The previously studied homogeneous (n,k,k−1)-perceptron learning algorithm is generalized to the permutably homogeneous (n,k,s)-perceptron learning algorithm with guaranteed convergence property. We also introduce a high capacity learning method that learns any permutably homogeneously separable k-valued function given as input.