원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.10 No.9
2015.09
pp.1-8
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Software Project Management (SPM) is one of the primary factors to software success or failure. Prediction of software development time is the key task for the effective SPM. The accuracy and reliability of prediction mechanisms is also important. In this paper, we compare different machine learning techniques in order to accurately predict the software time. Finally, by comparing the accuracy of different techniques, it has been concluded that Gaussian process algorithm has highest prediction accuracy among other techniques studied. Experimental results show this prediction approach is more effective.
목차
Abstract
1. Introduction
2. Related Work
2.1. Neural Networks
2.2. Linear Regression
2.3. Least Median Square
2.4. Gaussian Process
2.5. M5P
2.6. REPtree
2.7. Sequential Minimal Optimization
2.8. Multilayer Perceptron
3. Comparison of Machine Learning Algorithms to Predict Project Time
4. Experimental Results
5. Conclusions Second and Following Pages
Acknowledgements
References
1. Introduction
2. Related Work
2.1. Neural Networks
2.2. Linear Regression
2.3. Least Median Square
2.4. Gaussian Process
2.5. M5P
2.6. REPtree
2.7. Sequential Minimal Optimization
2.8. Multilayer Perceptron
3. Comparison of Machine Learning Algorithms to Predict Project Time
4. Experimental Results
5. Conclusions Second and Following Pages
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보