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Title: Extension of HUMANN for dealing with noise and with classes of different shape and size: a parametric study
Authors: García Báez, Patricio 
Araujo, Carmen Paz Suárez 
López, Pablo Fernández 
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 2001
Journal: Lecture Notes in Computer Science 
Conference: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 
Abstract: In this paper an extension of HUMANN (hierarchical unsupervised modular adaptive neural network) is presented together with a parametric study of this network in dealing with noise and with classes of any shape and size. The study has been made based on the two most noise dependent HUMANN parameters, λ and μ, using synthesised databases (bidimensional patterns with outliers and classes with different probability density distribution). In order to evaluate the robustness of HUMANN a Monte Carlo [1] analysis was carried out using the creation of separate data in given classes. The influence of the different parameters in the recovery of these classes was then studied.
ISBN: 978-3-540-42237-2
ISSN: 0302-9743
DOI: 10.1007/3-540-45723-2_11
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 2085 LNCS (PART 2), p. 96-103, (Enero 2001)
Appears in Collections:Actas de congresos
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