… Such a problem is common to machine learning models and is often referred to as domain
shift [1] (or distribution shift). To overcome the domain shift problem, two related areas have …

… detection method is proposed based on an ensemble learning approach which utilizes a set
of Bayesian networks, and each Bayesian network is built with both domain knowledge and …

… approaches of ensemble learning and … approach of ensemble learning, and we also give
an extra introduction for the combination of ensemble learning with other machine learning hot …

… domain-specific image classification problem with the following novelties: First, we propose
a method to utilize open-domain knowledge … Second, we propose a new ensemble learning …

… We propose two new methods for integrating domain knowledge for soccer outcome
prediction. We demonstrate the usefulness of our methods by benchmarking them against state-of-…

… However, in the absence of large datasets or for data that is not fully representative, we
show that the integration of domain knowledge offers advantages in predictive ability. …

… This paper presents a method for combining domain knowledge and machine learning (…
Whereas machine learning may discover patterns in interest domains that are too subtle for …

… Over the last couple of decades, multiple classifier systems, also called ensemble systems
… and machine learning community. This attention has been well deserved, as ensemble …

… knowledge embedded within and across source domains is … -level Alignment Network with
Ensemble learning (DANE) to … the intradomain shift, we present an intra-domain alignment by …

… domain expertise, making it hard to be widely applied. In contrast, machine learning method
… and the light-weight implementation, machine learning method has been widely applied by …