• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 1
  • Tagged with
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Novas abordagens para configura??es autom?ticas dos par?metros de controle em comit?s de classificadores

Nascimento, Diego Silveira Costa 05 December 2014 (has links)
Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-02-03T20:29:00Z No. of bitstreams: 1 DiegoSilveiraCostaNascimento_TESE.pdf: 3953454 bytes, checksum: 3237fa5d0296298ccc738a2ba7eab05e (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-02-03T23:54:37Z (GMT) No. of bitstreams: 1 DiegoSilveiraCostaNascimento_TESE.pdf: 3953454 bytes, checksum: 3237fa5d0296298ccc738a2ba7eab05e (MD5) / Made available in DSpace on 2016-02-03T23:54:37Z (GMT). No. of bitstreams: 1 DiegoSilveiraCostaNascimento_TESE.pdf: 3953454 bytes, checksum: 3237fa5d0296298ccc738a2ba7eab05e (MD5) Previous issue date: 2014-12-05 / Significativos avan?os v?m surgindo em pesquisas relacionadas ao tema de Comit?s de Classificadores. Os modelos que mais recebem aten??o na literatura s?o aqueles de natureza est?tica, ou tamb?m conhecidos por ensembles. Dos algoritmos que fazem parte dessa classe, destacam-se os m?todos que utilizam reamostragem dos dados de treinamento: Bagging, Boosting e Multiboosting. A escolha do tipo de arquitetura e dos componentes a serem recrutados n?o ? uma tarefa trivial, e tem motivado, ainda mais, o surgimento de novas propostas na tentativa de se construir tais modelos de forma autom?tica e, muitas delas, s?o baseadas em m?todos de otimiza??o. Muitas dessas contribui??es n?o t?m apresentado resultados satisfat?rios quando aplicadas a problemas mais complexos ou de natureza distinta. Em contrapartida, a tese aqui apresentada prop?e tr?s novas abordagens h?bridas para constru??o autom?tica em ensembles de classificadores: Incremento de Diversidade, Fun??o de Avalia??o Adaptativa e Meta-aprendizado para a elabora??o de sistemas de configura??o autom?tica dos par?metros de controle para os modelos de ensemble. Na primeira abordagem, ? proposta uma solu??o que combina diferentes t?cnicas de diversidade em um ?nico arcabou?o conceitual, na tentativa de se alcan?ar n?veis mais elevados de diversidade em ensemble, e com isso, melhor o desempenho de tais sistemas. J? na segunda abordagem, ? utilizado um algoritmo gen?tico para o design autom?tico de ensembles. A contribui??o consiste em combinar as t?cnicas de filtro e wrapper de forma adaptativa para evoluir uma melhor distribui??o do espa?o de atributos a serem apresentados aos componentes de um ensemble. E por fim, a ?ltima abordagem, que prop?e uma nova t?cnica de recomenda??o de arquitetura e componentes base em ensemble, via t?cnicas de meta-aprendizado tradicional e multirr?tulo. De forma geral os resultados s?o animadores, e corroboram com a tese de que ferramentas h?bridas s?o uma poderosa solu??o na constru??o de ensembles eficazes em problemas de classifica??o de padr?es / Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.

Page generated in 0.0783 seconds