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	Provedor de dados AgEcon (1) BABT (1) Autor Gulseven, Osman (1) Meena,K. (1) Rose,R. Reena (1) Suruliandi,A. (1) Wohlgenant, Michael K. (1) Palavra-chave Distance Metrics (2) Almost Ideal Demand System (1) C30 (1) C80 (1) Classifiers (1) Differentiated Products (1) Face Recognition (1) Food Security and Poverty (1) Hedonic Metrics (1) Local Texture Description Framework (1) Local Texture Description Framework-Based Modified Local Directional Number Pattern (1) Milk Demand. (1) Q11 (1) Q13 (1) Q18 (1) Rotterdam Model (1) Texture Descriptors (1) Mais... Tipo do documento Conference Paper or Presentation (1) Info:eu-repo/semantics/article (1) Ano 2016 (1) 2010 (1) País Brazil (1) United States (1) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última A Hedonic Metric Approach to Estimating the Demand for Differentiated Products: An Application to Retail Milk Demand AgEcon Gulseven, Osman; Wohlgenant, Michael K.. This article introduces the Hedonic Metric (HM) approach as an original method to model the demand for differentiated products. Using this approach, initially we create an n-dimensional hedonic space based on the characteristic information available to consumers. Next, we allocate products into this space and estimate the elasticities using distances. What distinguishes our model from traditional demand models such as Almost Ideal Demand System (AIDS) and Rotterdam Model is the way we link elasticities with product characteristics. Moreover, our model significantly reduces the number of parameters to be estimated, thereby making it possible to estimate large number of differentiated products in a single demand system. We applied our model to estimate the... Tipo: Conference Paper or Presentation Palavras-chave: Hedonic Metrics; Distance Metrics; Rotterdam Model; Almost Ideal Demand System; Differentiated Products; Milk Demand.; Food Security and Poverty; C30; C80; Q11; Q13; Q18. Ano: 2010 URL: http://purl.umn.edu/91675 An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition BABT Rose,R. Reena; Meena,K.; Suruliandi,A.. ABSTRACT Texture is one of the chief characteristics of an image. In recent years, local texture descriptors have garnered attention among researchers in describing effective texture patterns to demarcate facial images. A feature descriptor titled Local Texture Description Framework-based Modified Local Directional Number pattern (LTDF_MLDN), capable of encoding texture patterns with pixels that lie at dissimilar regions, has been proposed recently to describe effective features for face images. However, the role of the descriptor can differ with different classifiers and distance metrics for diverse issues in face recognition. Hence, in this paper, an extensive evaluation of the LTDF_MLDN is carried out with an Extreme Learning Machine (ELM), a Support... Tipo: Info:eu-repo/semantics/article Palavras-chave: Face Recognition; Texture Descriptors; Local Texture Description Framework; Local Texture Description Framework-Based Modified Local Directional Number Pattern; Classifiers; Distance Metrics. Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300405 Registros recuperados: 2 Primeira ... 1 ... Última

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