Distribution of the Discriminant Function

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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Quadratic Discriminant Analysis of Spatially Correlated Data

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2001.6.1.15212 ◽

2001 ◽

Vol 6 (2) ◽

pp. 15-28 ◽

Cited By ~ 2

Author(s):

K. Dučinskas ◽

J. Šaltytė

Keyword(s):

Discriminant Function ◽

Error Rate ◽

Gaussian Random Field ◽

Correlated Data ◽

Covariance Matrices ◽

Classification Rule ◽

Spatial Correlations ◽

Linear Quadratic ◽

Spatial Correlation Function ◽

Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

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Estimating the probability of a class at a point by the approximation of one discriminant function

Автоматика и телемеханика ◽

10.31857/s000523100001446-4 ◽

2018 ◽

pp. 46-58

Author(s):

V. Zenkov ◽

Keyword(s):

Discriminant Function

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Synthetic Discriminant Function Performance Versus Filter Count

10.21236/ada377336 ◽

2000 ◽

Author(s):

Michael J. Vrabel

Keyword(s):

Discriminant Function ◽

Synthetic Discriminant Function

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The Relative Classificatory Ability of the Linear Discriminant Function and the Bayesian Taxonomic Procedure

The Journal of Experimental Education ◽

10.1080/00220973.1974.10806320 ◽

1974 ◽

Vol 43 (2) ◽

pp. 56-62

Author(s):

Norval Frederick Pohl

Keyword(s):

Discriminant Function ◽

Linear Discriminant Function ◽

Linear Discriminant

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Description of Echolocation Call Parameters for Urban Bats in Vietnam as a Step Towards a More Integrated Acoustic Monitoring of Urban Wildlife in Southeast Asia

Diversity ◽

10.3390/d13010018 ◽

2021 ◽

Vol 13 (1) ◽

pp. 18

Author(s):

Long Kim Pham ◽

Bang Van Tran ◽

Quy Tan Le ◽

Trung Thanh Nguyen ◽

Christian C. Voigt

Keyword(s):

Discriminant Function ◽

Discriminant Function Analysis ◽

Function Analysis ◽

Quantitative Description ◽

Southeast Asian ◽

Asian Countries ◽

Echolocation Call ◽

Echolocation Calls ◽

Pilot Work ◽

Southeast Asian Countries

This study is the first step towards more systematic monitoring of urban bat fauna in Vietnam and other Southeast Asian countries by collecting bat echolocation call parameters in Ho Chi Minh and Tra Vinh cities. We captured urban bats and then recorded echolocation calls after releasing in a tent. Additional bat’s echolocation calls from the free-flying bats were recorded at the site where we captured bat. We used the obtained echolocation call parameters for a discriminant function analysis to test the accuracy of classifying these species based on their echolocation call parameters. Data from this pilot work revealed a low level of diversity for the studied bat assemblages. Additionally, the discriminant function analysis successfully classified bats to four bat species with an accuracy of >87.4%. On average, species assignments were correct for all calls from Taphozous melanopogon (100% success rate), for 70% of calls from Pipistrellus javanicus, for 80.8% of calls from Myotis hasseltii and 67.3% of calls from Scotophilus kuhlii. Our study comprises the first quantitative description of echolocation call parameters for urban bats of Vietnam. The success in classifying urban bats based on their echolocation call parameters provides a promising baseline for monitoring the effect of urbanization on bat assemblages in Vietnam and potentially also other Southeast Asian countries.

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