Naslov
Predicting More from Less: Synergies of Learning

Autori
Kocaguneli, Ekrem ; Čukić, Bojan ; Lu, Huihua

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo

Izvornik
Proc. of 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering / Unknown - New York (NY) : Institute of Electrical and Electronics Engineers (IEEE), 2013, 42-48

ISBN
978-1-4673-6437-9

Skup
2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering

Mjesto i datum
San Francisco (CA), Sjedinjene Američke Države, 25.05.2013. - 26.05.2013

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
none

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA