Perceptron Learning of Modified Quadratic Discriminant Function

2011 ◽  
Author(s):  
Tong-Hua Su ◽  
Cheng-Lin Liu ◽  
Xu-Yao Zhang
Keyword(s):  
Discriminant Function ◽  
Quadratic Discriminant Function ◽  
Perceptron Learning

scholarly journals Sensitivity Analysis of the Quadratic Discriminant Function for Predicting Pregnancy Outcomes

2020 ◽  
pp. 60-69
Author(s):  
Killian Asampana Asosega ◽  
David Adedia ◽  
Atinuke O. Adebanji
Keyword(s):  
Birth Outcomes ◽  
Live Birth ◽  
Discriminant Function ◽  
Pregnancy Outcomes ◽  
Maternal Age ◽  
Characteristic Curve ◽  
Developed Countries ◽  
Positive Effects ◽  
Gestational Period ◽  
Quadratic Discriminant Function

The prevalence rate of stillbirth is ten times higher in developing countries relative to developed countries with a 2016 rate of 18 percent in Ghana. This study employed the Quadratic Discriminant Function for discriminating and classifying of pregnancy outcomes based on some predictors. The study further examined the sensitivity of the Quadratic Discriminant Function in predicting pregnancy outcomes with variations in the training and test samples of deliveries recorded in a hospital in Accra, Ghana. The study considered the scenarios; 50:50, 60:40, 70:30 and 75:25 ratios of training sets to testing sets. Predictor variables on both maternal factors (maternal age, parity and gravida) and fetus variables (weight at birth and gestational period) were all statistically significant (P < .01) in discriminating between live birth and stillbirth. Results showed that maternal age had a negative effect on the live birth outcomes, while parity, gravida, gestational period and fetus weight recorded positive effects on live birth outcomes. The 75:25 ratio outperformed the other ratios in discriminating between live and stillbirth based on the Actual Error Rate of 7.28% compared to 7.81%, 12.14% and 13.79% for the 50:50, 70:30 and 60:40 ratios respectively whereas, the receiver operating characteristic curve shows the 70:30 (AUC= 0.9233) ratio outperformed the others. The study recommend the use of either the 70:30 or 75:25 training to test ratios for classification and discrimination related problems. Moreover, further research to establish the power of the respective training to test sample ratios with other statistical classification tools and more socio-economic variables can be considered.

scholarly journals Classification of all-rounders in limited over cricket - a machine learning approach

2021 ◽  
Vol 6 (4) ◽  
pp. 295-306
Author(s):  
Ananda B. W. Manage ◽  
Ram C. Kafle ◽  
Danush K. Wijekularathna
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Discriminant Function ◽  
Linear Discriminant Function ◽  
Classification Rule ◽  
Support Vector ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Quadratic Discriminant Function

In cricket, all-rounders play an important role. A good all-rounder should be able to contribute to the team by both bat and ball as needed. However, these players still have their dominant role by which we categorize them as batting all-rounders or bowling all-rounders. Current practice is to do so by mostly subjective methods. In this study, the authors have explored different machine learning techniques to classify all-rounders into bowling all-rounders or batting all-rounders based on their observed performance statistics. In particular, logistic regression, linear discriminant function, quadratic discriminant function, naïve Bayes, support vector machine, and random forest classification methods were explored. Evaluation of the performance of the classification methods was done using the metrics accuracy and area under the ROC curve. While all the six methods performed well, logistic regression, linear discriminant function, quadratic discriminant function, and support vector machine showed outstanding performance suggesting that these methods can be used to develop an automated classification rule to classify all-rounders in cricket. Given the rising popularity of cricket, and the increasing revenue generated by the sport, the use of such a prediction tool could be of tremendous benefit to decision-makers in cricket.

G-estimates of the quadratic discriminant function

1989 ◽  
Vol 41 (12) ◽  
pp. 1469-1473 ◽  
Author(s):  
V. L. Girko ◽  
T. V. Pavlenko
Keyword(s):  
Discriminant Function ◽  
Quadratic Discriminant Function

How non-normality affects the quadratic discriminant function

1979 ◽  
Vol 8 (13) ◽  
pp. 1285-1301 ◽  
Author(s):  
William R. Clarke ◽  
Peter A. Lachenbruch ◽  
Barabara Broffitt
Keyword(s):  
Discriminant Function ◽  
Quadratic Discriminant Function
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