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Showing 1 to 20 of 510 for “"Discriminant Analysis"”.
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Linear discriminant analysis
Linear discriminant analysis is the classification of an individual as having arisen from one or the other of two populations on the basis of a scalar linear function of measurements of the individual. This paper is a population and large sample study of linear discriminant analysis. The population …
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Classification and discriminant analysis
… Three major techniques of classification and discriminant analysis are presented: linear discriminant analysis, quadratic discriminant procedures and logistic regression. Logistic regression is reviewed in its general framework and as a classification tool. A few articles on the comparison of …
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Agricultural loan evaluation with discriminant analysis
As each year passes, the farmers' needs for adequate credit adapted to their particular type of businesses, become more evident, The shift in the past half century to a mechanized agriculture has greatly increased farm credit needs. The total real estate assets of farmers have increased from a low …
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Selected topics in statistical discriminant analysis.
… consists of three selected topics in statistical discriminant analysis: dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction method to determine a low-dimensional hyperplane that preserves or nearly preserves the …
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Modified Discriminant Analysis of School Organizations
Made available in DSpace on 2014-12-05T22:27:18Z (GMT). No. of bitstreams: 1 0009039.pdf: 5205040 bytes, checksum: 6fa724539b07661a633dd2e8b9c5a610 (MD5) Previous issue date: 1954
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Penalized discriminant analysis for multivariate functional data
We introduce a penalized discriminant analysis method for multivariate functional data supported on compact 1D domains, motivated by an application that aims to identify subjects with poor cognitive status from diffusion MRI data. By leveraging a connection to the optimal scoring problem, we bypass …
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Unified Framework for Matrix-Variate Linear Discriminant Analysis
The linear discriminant analysis (LDA) is a feature extractor used in classification of high-dimensional data in a wide range of applications. In the classification of matrix-variate data, LDA can be used on the vectorized representation of the data in the commonly called one-dimensional LDA …
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Dynamic Discriminant Analysis with Applications in Computational Surgery
… fields has been impeded by the lack of a dynamic discriminant analysis technique capable of classifying data given systems with overwhelming similarity. Methods: Four new machine learning algorithms were developed (DLS, DPP, RELIEF-RBF, and Intent Vectors). These algorithms were then applied to …
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The Use of Predictive Composites in Multiple Discriminant Analysis
Made available in DSpace on 2014-12-12T19:53:32Z (GMT). No. of bitstreams: 1 7606825.pdf: 4340940 bytes, checksum: 017989d72a2e333a0af3702681056458 (MD5) Previous issue date: 1975
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A Multiple Discriminant Analysis Approach to the Development of Form Constancy
Made available in DSpace on 2014-12-08T23:02:09Z (GMT). No. of bitstreams: 1 7013371.pdf: 3051346 bytes, checksum: cc3bf7efda3ffdb328d681f6894f6b4f (MD5) Previous issue date: 1969
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Using Geovisual Analytics to investigate the performance of Geographically Weighted Discriminant Analysis
Geographically Weighted Discriminant Analysis (GWDA) is a method for prediction and analysis of categorical spatial data. It is an extension of Linear Discriminant Analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This is also referred …
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Using NMR spectroscopy and linear discriminant analysis to molecular profile varietal honey
… (“fingerprints”) and combined them with linear discriminant analysis (LDA) to predict the varietal, country, and region of varietal honey. As a part of the project, we investigated whether an adjustment to the spectral data based on a signal-to-noise cutoff would provide better predictive …
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Discriminant analysis : a review of its application to the classificationof grape cultivars
… techniques, such as principal component analysis, cluster analysis, correspondence analysis and discriminant analysis were applied. Discriminant analysis resulted in the most appropriate technique for the problem at hand and therefore an in depth study of this technique was undertaken. …
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The predictive ability of discriminant analysis to identify takeover targets for portfolio selection
This study utilizes the discriminant analysis technique in the development of a model able to predict acquisition targets. The model is tested in a portfolio selection setting to determine its ability to identify portfolios capable of performance superior to that of the market. The sample in the …
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Analytic Study of Performance of Error Estimators for Linear Discriminant Analysis with Applications in Genomics
… and mixed moments, in the context of the Linear Discriminant Analysis (LDA) classification rule. In the first part of this dissertation, we obtain the joint sampling distribution of the actual and estimated errors under a general parametric Gaussian assumption. Exact results are provided in the …
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Performance Evaluation of Logistic Regression, Linear Discriminant Analysis, and Classification and Regression Trees Under Controlled Conditions
<p>Logistic Regression (LR), Linear Discriminant Analysis (LDA), and Classification and Regression Trees (CART) are common classification techniques for prediction of group membership. Since these methods are applied for similar purposes with different procedures, it is important to evaluate the …
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The Use of Genetic Polymorphisms and Discriminant Analysis in Evaluating Genetic Polymorphisms as a Predictor of Population
Discriminant analysis is a procedure for identifying the relationships between qualitative criterion variables and quantitative predictor variables. Data bases of genetic polymorphisms are currently available that group such polymorphisms by ethnic origin or nationality. Such information could be …
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