Predicting More from Less: Synergies of Learning (CROSBI ID 606992)
Prilog sa skupa u zborniku | ostalo | međunarodna recenzija
Podaci o odgovornosti
Kocaguneli, Ekrem ; Čukić, Bojan ; Lu, Huihua
engleski
Predicting More from Less: Synergies of Learning
Thanks to the ever increasing importance of project data, its collection has been one of the primary focuses of software organizations. Data collection activities have resulted in the availability of massive amounts of data through software data repositories. This is great news for the predictive modeling research in software engineering. However, widely used supervised methods for predictive modeling require labeled data that is relevant to the local context of a project. This requirement cannot be met by many of the available data sets, introducing new challenges for software engineering research. How to transfer data between different contexts? How to handle insufficient number of labeled instances? In this position paper, we investigate synergies between different learning methods (transfer, semi-supervised and active learning) which may overcome these challenges.
none
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
42-48.
2013.
objavljeno
Podaci o matičnoj publikaciji
Proc. of 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering
unknown
New York (NY): Institute of Electrical and Electronics Engineers (IEEE)
978-1-4673-6437-9
Podaci o skupu
2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering
predavanje
25.05.2013-26.05.2013
San Francisco (CA), Sjedinjene Američke Države