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Fuzzy heterogeneous neurons for imprecise classification problems
dc.contributor.author | Valdés Ramos, Julio José |
dc.contributor.author | Belanche Muñoz, Luis Antonio |
dc.contributor.author | Alquézar Mancho, René |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2020-03-30T10:14:35Z |
dc.date.available | 2020-03-30T10:14:35Z |
dc.date.issued | 2000-02 |
dc.identifier.citation | Valdés, J.; Belanche, L.; Alquézar, R. Fuzzy heterogeneous neurons for imprecise classification problems. "International journal of intelligent systems", Febrer 2000, vol. 15, núm. 3, p. 265-276. |
dc.identifier.issn | 0884-8173 |
dc.identifier.uri | http://hdl.handle.net/2117/182212 |
dc.description.abstract | In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and different neural architectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classification and diagnostic problems are here illustrated by experiments with data from a real-world domain in the field of environmental studies. These experiments show that such neurons can both learn and classify complex data very effectively in the presence of uncertain information. |
dc.format.extent | 12 p. |
dc.language.iso | eng |
dc.publisher | Wiley |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Fuzzy systems |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Machine learning |
dc.subject.other | Environmental science computing |
dc.subject.other | Fuzzy neural nets |
dc.subject.other | Learning (artificial intelligence) |
dc.subject.other | Pattern classification |
dc.subject.other | Uncertainty handling |
dc.title | Fuzzy heterogeneous neurons for imprecise classification problems |
dc.type | Article |
dc.subject.lemac | Sistemes borrosos |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.identifier.doi | 10.1002/(SICI)1098-111X(200003)15:3<265::AID-INT7>3.0.CO;2-I |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://doi.org/10.1002/(SICI)1098-111X(200003)15:3<265::AID-INT7>3.0.CO;2-I |
dc.rights.access | Open Access |
local.identifier.drac | 1630396 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Valdés, J.; Belanche, L.; Alquézar, R. |
local.citation.publicationName | International journal of intelligent systems |
local.citation.volume | 15 |
local.citation.number | 3 |
local.citation.startingPage | 265 |
local.citation.endingPage | 276 |
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