830 resultados para Weights selection
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The goal of this dissertation thesis is the estimation of the Saturnian satellites ephemerides using optical data of Cassini. In the first part we describe the software employed for the reduction of the images showing its main features and the accuracy that can be achieved comparing the results with published astrometry. Afterwards we describe the orbit determination problem (ODP) with particular focus on the weights selection for the estimation process. The third chapter describes the dynamical model used and the sources of potential errors in the residuals. The model have been validated trying to replicate JPL's published ephemerides SAT365, SAT375, SAT389 and SAT409. The final part investigates the residuals and the estimated ephemerides with particular focus on the giant moon Titan, the only in the solar system with an atmosphere other than the Earth. No astrometry have been retrieved in literature of Titan using optical observables, thus this represents one of the first investigations of the giant.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).
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Service provider selection has been said to be a critical factor in the formation of supply chains. Through successful selection companies can attain competitive advantage, cost savings and more flexible operations. Service provider management is the next crucial step in outsourcing process after the selection has been made. Without proper management companies cannot be sure about the level of service they have bought and they may suffer from service provider's opportunistic behavior. In worst case scenario the buyer company may end up in locked-in situation in which it is totally dependent of the service provider. This thesis studies how the case company conducts its carrier selection process along with the criteria related to it. A model for the final selection is also provided. In addition, case company's carrier management procedures are reflected against recommendations from previous researches. The research was conducted as a qualitative case study on the principal company, Neste Oil Retail. A literature review was made on outsourcing, service provider selection and service provider management. On the basis of the literature review, this thesis ended up recommending Analytic hierarchy process as the preferred model for the carrier selection. Furthermore, Agency theory was seen to be a functional framework for carrier management in this study. Empirical part of this thesis was conducted in the case company by interviewing the key persons in the selection process, making observations and going through documentations related to the subject. According to the results from the study, both carrier selection process as well as carrier management were closely in line with suggestions from literature review. Analytic hierarchy process results revealed that the case company considers service quality as the most important criteria with financial situation and price of service following behind with almost identical weights with each other. Equipment and personnel was seen as the least important selection criterion. Regarding carrier management, the study resulted in the conclusion that the company should consider engaging more in carrier development and working towards beneficial and effective relationships. Otherwise, no major changes were recommended for the case company processes.
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An appropriate supplier selection and its profound effects on increasing the competitive advantage of companies has been widely discussed in supply chain management (SCM) literature. By raising environmental awareness among companies and industries they attach more importance to sustainable and green activities in selection procedures of raw material providers. The current thesis benefits from data envelopment analysis (DEA) technique to evaluate the relative efficiency of suppliers in the presence of carbon dioxide (CO2) emission for green supplier selection. We incorporate the pollution of suppliers as an undesirable output into DEA. However, to do so, two conventional DEA model problems arise: the lack of the discrimination power among decision making units (DMUs) and flexibility of the inputs and outputs weights. To overcome these limitations, we use multiple criteria DEA (MCDEA) as one alternative. By applying MCDEA the number of suppliers which are identified as efficient will be decreased and will lead to a better ranking and selection of the suppliers. Besides, in order to compare the performance of the suppliers with an ideal supplier, a “virtual” best practice supplier is introduced. The presence of the ideal virtual supplier will also increase the discrimination power of the model for a better ranking of the suppliers. Therefore, a new MCDEA model is proposed to simultaneously handle undesirable outputs and virtual DMU. The developed model is applied for green supplier selection problem. A numerical example illustrates the applicability of the proposed model.
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The lack of research of private real estate is a well-known problem. Earlier studies have mostly concentrated on the USA or the UK. Therefore, this master thesis offers more information about the performance and risk associated with private real estate investments in Nordic countries, but especially in Finland. The structure of this master thesis is divided into two independent sections based on the research questions. In first section, database analysis is performed to assess risk-return ratio of direct real estate investment for Nordic countries. Risk-return ratios are also assessed for different property sectors and economic regions. Finally, review of diversification strategies based on property sectors and economic regions is performed. However, standard deviation itself is not usually sufficient method to evaluate riskiness of private real estate. There is demand for more explicit assessment of property risk. One solution is property risk scoring. In second section risk scorecard based tool is built to make different real estate comparable in terms of risk. In order to do this, nine real estate professionals were interviewed to enhance the structure of theory-based risk scorecard and to assess weights for different risk factors.
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A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Dados de 23.120 animais da raça Nelore foram utilizados para estimar herdabilidade e correlações genéticas para a idade ao primeiro parto, o ganho em peso da desmama ao ano e do ano ao sobreano, o peso à desmama, o peso ao ano, o peso ao sobreano e os pesos aos 2 e aos 5 anos de idade. Utilizou-se o método da máxima verossimilhança restrita, em análise multicaracterística. As herdabilidades estimadas para idade ao primeiro parto, ganho da desmama ao ano, ganho do ano ao sobreano, peso à desmama, peso ao ano, peso ao sobreano e peso aos 2 aos 5 anos foram de 0,17 ± 0,01; 0,23 ± 0,03; 0,25 ± 0,03; 0,28 ± 0,02; 0,26 ± 0,03; 0,30 ± 0,03; 0,32 ± 0,02 e 0,36 ± 0,04, respectivamente. Correlações genéticas baixas e negativas foram estimadas entre a idade ao primeiro parto e os pesos medidos em diferentes idades, que variaram de -0,26 a -0,14. As correlações genéticas estimadas entre a idade ao primeiro parto e os ganhos de peso também foram negativas, porém levemente superiores (-0,29 e -0,32). Os resultados indicam que a seleção para maior ganho de peso pode reduzir a idade ao primeiro parto e aumentar o peso adulto de fêmeas da raça Nelore. Mudança genética mais rápida para diminuição da idade ao primeiro parto das fêmeas pode ser obtida com a inclusão dessa característica nos índices de seleção.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)