A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Toward a software development model for automatic marking software
Proceedings of the 35th annual ACM SIGUCCS fall conference
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
An overview of statistical learning theory
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This paper reports an automated engineering assignments marking system using Support Vector Machines (SVMs). A typical engineering assignment consists of more than just text. It may also contain mathematical equations, pictures, diagrams, charts, algorithms, or even programming source codes. These elements have to be taken into consideration in the marking process. The automated marking process is more consistent. The system learns how to mark based on grades given for the first few scripts. The system would learn the marking scheme and mark the subsequent scripts automatically. A prototype system to mark equations and short answers is prototyped and evaluated. This system forms the foundation to be expanded into a full fledge automated assessment system.