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Learning with Permutably Homogeneous Multiplevalued Multiplethreshold 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 kvalued function given as input.
On the number of multilinear partitions and the computing capacity of multiplevalued multiplethreshold perceptrons
 In TwentyNinth IEEE International Symposium on MultipleValued Logic
, 1999
"... On the number of multilinear partitions and the computing capacity of multiplevalued multiplethreshold perceptrons ..."
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On the number of multilinear partitions and the computing capacity of multiplevalued multiplethreshold perceptrons
Minimization of Multivalued Multithreshold Perceptrons Using Genetic Algorithms
, 1998
"... We address the problem of computing and learning multivalued multithreshold perceptrons. Every ninput kvalued 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 ninput kvalued 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 multiplevalued logic function, that is to minimize the number of thresholds. Experimental results show that the genetic algorithm nd optimal solutions in most cases.