Gender Assignment of Westland Petrels (Procellaria westlandica) Using Linear Discriminant Function Analysis

2011 ◽  
Vol 123 (4) ◽  
pp. 720-725 ◽  
Author(s):  
Todd J. Landers ◽  
Todd E. Dennis ◽  
Mark E. Hauber
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Linear Discriminant Function ◽  
Gender Assignment ◽  
Linear Discriminant ◽  
Linear Discriminant Function Analysis

Characteristics of Permanent Teeth in Persons with Trisomy G

1976 ◽  
Vol 55 (4) ◽  
pp. 633-638 ◽  
Author(s):  
B. Prahl-Andersen ◽  
J. Oerlemans
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Permanent Teeth ◽  
Tooth Size ◽  
Linear Discriminant ◽  
Second Molar ◽  
The Mean ◽  
Size And Morphology ◽  
Linear Discriminant Function Analysis

Tooth size and morphology in 35 participants with trisomy G and in 33 controls have been studied. Special attention has been paid to the mean cusp pattern of the upper first and second molar. The classification matrix for the linear discriminant function analysis between participants with trisomy G and controls based on five selected variables showed three misclassifications.

scholarly journals Use of neural networks to diagnose acute myocardial infarction. I. Methodology

1996 ◽  
Vol 42 (4) ◽  
pp. 604-612 ◽  
Author(s):  
J S Jørgensen ◽  
J B Pedersen ◽  
S M Pedersen
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Acute Myocardial Infarction ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Principal Component ◽  
Clinical Problem ◽  
Usual Practice ◽  
Linear Discriminant

Abstract We investigated several aspects of using neural networks as a diagnostic tool: the design of an optimal network, the amount of patients' data needed to train the network, the question of training the network optimally while avoiding overfitting, and the influence of redundant variables. The specific clinical problem chosen for illustration was the diagnosis of acute myocardial infarction, given only the electrocardiogram and the concentration of potassium in serum at the time of admission. We found that, in contrast to usual practice, the termination of the training process should be based on the generalization performance and not on the training performance. We also found that a principal component analysis can be used to eliminate redundant variables, thereby reducing the data space. The diagnostic performance of the neural network we used was 78%--superior to that of linear discriminant function analysis but similar to that of quadratic discriminant function analysis.

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