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Abstract: We present a new learning machine model for classi cation problems, based on decompositions of multiclass classi cation problems in sets of two-class subproblems, assigned to non linear dichotomizers that learn their task independently of each other. The experimentation performed on classical data sets, shows that this learning machine model achieves signi cant performance improvements over MLP, and previous classi ers models based on decomposition of polychotomies into dichotomies. The... (Update)
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.... for implementing the dichotomizers are linear (as in [3] or Parallel Non linear Dichotomizers (PND) if the dichotomizers are non linear [17, 38]. Parallel Non linear Dichotomizers (PND) are multiclassifiers based on the decomposition of polychotomies into dichotomies, using...
.... for implementing the dichotomizers are linear (as in [3] or Parallel Non linear Dichotomizers (PND) if the dichotomizers are non linear [17, 38]. Parallel Non linear Dichotomizers (PND) are multiclassi ers based on the decomposition of polychotomies into dichotomies, using...
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BibTeX entry: (Update)
F. Masulli and G. Valentini. Parallel Non linear Dichotomizers. In IJCNN2000, The IEEE-INNS-ENNS International Joint Conference on Neural Networks, volume 2, pages 29-33, Como, Italy, 2000. http://citeseer.ist.psu.edu/masulli00parallel.html More
@misc{ masulli00parallel,
author = "F. Masulli and G. Valentini",
title = "Parallel Non linear Dichotomizers",
text = "F. Masulli and G. Valentini. Parallel Non linear Dichotomizers. In IJCNN2000,
The IEEE-INNS-ENNS International Joint Conference on Neural Networks, volume
2, pages 29-33, Como, Italy, 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/masulli00parallel.html" }
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