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Considering the variability of bay coat colour, we aimed to investigate the association of different shades of bay with ASIP and MC1R genotype combinations and we studied the discrimination between th...
We introduce a technique for extending the classical method of Linear Discriminant Analysis to data sets where the predictor variables are curves or functions. This procedure, which we call functional...
In high-dimensional classification problems, one is often interested in ?? finding a few important discriminant directions in order to reduce the dimensionality.Fisher’s linear discriminant analysis(L...
In this paper, we introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “near...
Sparse Discriminant Analysis     Sparse  Discriminant Analysis       font style='font-size:12px;'> 2015/8/21
We consider the problem of performing interpretable classification in the high-dimensional setting, in which the number of features is very large and the number of observations is limited. This settin...
We used the discriminant analysis of curd cheese during storage by Fourier transform near infrared spectroscopy method (FT-NIRs). Olomouc curd cheese samples were stored at 5 and at 20°C during seven ...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learnin...
Abstract: This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\O$ and...
Most classification methods provide either a prediction of group membership or an assessment of class membership probability. In the case of two-group classifica-tion the predicted probability can be ...
Most classification methods provide either a prediction of group membership or an assessment of class membership probability. In the case of two-group classification the predicted probability can be d...
More than three years of measurements of aerosol size-distribution and different gas and meteorological parameters made in Po Valley, Italy were analysed for this study to examine which of the meteoro...
Extended Data Envelopment Analysis/Discriminant Analysis (DEA/DA), is a widely applied approach for firstly the classification of observations into groups and secondly the prediction of the membership...
To represent an optimization problem as a linear programming (LP), there are several assumptions needed. Among them, there is the ”Deterministic” assumption that requires all the coefficients of the L...
Discriminant analysis of imprecise data      Data Envelopment Analysis  Imprecise Data       font style='font-size:12px;'> 2010/9/15
Data envelopment analysis technique which is developed based on the mathematical programming, evaluates the relative efficiency of a set of homogeneous decision making units. This paper shows the meth...
Quantitatively determining the sources of dune sand is one of the problems necessarily and urgently to be solved in aeolian landforms and desertification research. Based on the granulometr...

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