Introduction to the theory of neural computation
Introduction to the theory of neural computation
Predicting graduate student success: a comparison of neural networks and traditional techniques
Computers and Operations Research
Neural networks and logistic regression: Part I
Computational Statistics & Data Analysis
Neural network applications in business: a review and analysis of the literature (1988-95)
Decision Support Systems
Expert Systems with Applications: An International Journal
Modeling children's mathematical gift by neural networks and logistic regression
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Over the past several years, there is tremendous increase in the number of applicants to business schools and hence adequately measuring the potential of these students with regard to their academic performance is an important process of admission decision for any business school. In the present study, an analysis is carried out to predict the academic performance of business school graduates using neural networks and traditional statistical techniques and results are compared to evaluate the performance of these techniques. The underlying constructs in a traditional business school curriculum are also identified and its relevance with the various elements of admission process is presented.