109 resultados para Cadeia de Markov
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The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.
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This paper addresses the H ∞ state-feedback control design problem of discretetime Markov jump linear systems. First, under the assumption that the Markov parameter is measured, the main contribution is on the LMI characterization of all linear feedback controllers such that the closed loop output remains bounded by a given norm level. This results allows the robust controller design to deal with convex bounded parameter uncertainty, probability uncertainty and cluster availability of the Markov mode. For partly unknown transition probabilities, the proposed design problem is proved to be less conservative than one available in the current literature. An example is solved for illustration and comparisons. © 2011 IFAC.
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This paper deals with exponential stability of discrete-time singular systems with Markov jump parameters. We propose a set of coupled generalized Lyapunov equations (CGLE) that provides sufficient conditions to check this property for this class of systems. A method for solving the obtained CGLE is also presented, based on iterations of standard singular Lyapunov equations. We present also a numerical example to illustrate the effectiveness of the approach we are proposing.
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The aim of this study was to evaluate the effects of yeast cell wall extract (YCW) in dry diet on the fecal microbiota, concentration of short-chain fatty acids (SCFA) and on the odor reduction of cats feces. We used 20 animals of both sexes, randomly assigned to four treatments and five repetitions totaling 20 experimental units: 1) dry commercial diet (control); 2) control + 0.2%, 3) control + 0.4%, and 4) control + 0.6% of YCW in dry matter. Enterobacteriaceae and lactic acid bacteria, fecal concentration of acetic, propionic and butyric acids, ammonia nitrogen and sensory panel were performed. There were no significant differences (p> 0.05) for bacterial counts and the concentration of SCFA and ammonia, but in sensory panel a reduction in the odor of feces could be noted with the use of 0.2% of YCW. We concluded that the addition of up to 0.6% YCW had no effect on the microbiology and the concentration of fatty acids, but there is potential for its use as an additive because of the improvement in the odor of feces. However, further studies are needed to understand the mechanisms of action and the effects of prebiotics for domestic cats.
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Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Comunicação - FAAC
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Engenharia e Ciência de Alimentos - IBILCE
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Geografia - FCT
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)