Kvstas Blekas

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The fuzzy min-max classiication network constitutes a promisimg pattern recognition approach that is based on hy-berbox fuzzy sets and can be incrementally trained requiring only one pass through the training set. The deenition and operation of the model considers only attributes assuming continuous values. Therefore, the application of the fuzzy min-max(More)
A neural network classiier using fuzzy set representation of pattern classes is presented. Network construction and learning is performed incrementally in a single pass by building an aggregate of space-lling regions that constitutes a simpliied variant of the construction known as Dirichlet tesselation (or Voronoi diagram). Each region is delimited by a(More)
OBJECTIVES This paper proposes a greedy algorithm for learning a mixture of motifs model through likelihood maximization, in order to discover common substrings, known as motifs, from a given collection of related biosequences. METHODS The approach sequentially adds a new motif component to a mixture model by performing a combined scheme of global and(More)
The fuzzy min-max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. Two versions have been proposed each one suitable for supervised and unsupervised learning respectively. In this paper a modiied approach(More)
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