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Learning with Permutably Homogeneous Multiple-valued Multiple-threshold Perceptrons
, 2000
"... The …n; k; s†-perceptrons partition the input space V 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; ..."
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Cited by 5 (2 self)
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The …n; k; s†-perceptrons partition the input space V 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.
On the number of multilinear partitions and the computing capacity of multiple-valued multiple-threshold perceptrons
- In Twenty-Ninth IEEE International Symposium on Multiple-Valued Logic
, 1999
"... On the number of multilinear partitions and the computing capacity of multiple-valued multiple-threshold perceptrons ..."
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On the number of multilinear partitions and the computing capacity of multiple-valued multiple-threshold perceptrons
Minimization of Multivalued Multithreshold Perceptrons Using Genetic Algorithms
, 1998
"... We address the problem of computing and learning multivalued multithreshold perceptrons. Every n-input k-valued logic function can be implemented using a (k; s)-perceptron, for some number of thresholds s. We propose a genetic algorithm to search for an optimal (k; s)-perceptron that e ciently reali ..."
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We address the problem of computing and learning multivalued multithreshold perceptrons. Every n-input k-valued logic function can be implemented using a (k; s)-perceptron, for some number of thresholds s. We propose a genetic algorithm to search for an optimal (k; s)-perceptron that e ciently realizes a given multiple-valued logic function, that is to minimize the number of thresholds. Experimental results show that the genetic algorithm nd optimal solutions in most cases.
