Flexible Transfer Learning under Support and Model Shift
نویسندگان
چکیده
In a classical transfer learning setting, we have sufficient fully labeled data from the source domain (or the training domain) where we fully observe the data points X, and all corresponding labels Y tr are known. On the other hand, we are given data points, X, from the target domain (or the test domain), but few or none of the corresponding labels, Y , are given. The source and the target domains are related but not identical, thus the joint distributions, P (X, Y ) and P (X, Y ), are different across the two domains.
منابع مشابه
Interfirm Alliance Interactions and knowledge Learning: A Conceptual Research Model
Alliance raises many knowledge transfer and interfirm learning issues that have implications for how the alliance partners manage their cooperative learning activities in the alliance system. Many of these implications are grounded in the assumption that partners in the alliances have routines for transferring knowledge, learning, gaining management efficiencies. Thus organisations can support ...
متن کاملReal-time Scheduling of a Flexible Manufacturing System using a Two-phase Machine Learning Algorithm
The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...
متن کاملGeneralization Bounds for Transfer Learning under Model Shift
Transfer learning (sometimes also referred to as domain-adaptation) algorithms are often used when one tries to apply a model learned from a fully labeled source domain, to an unlabeled target domain, that is similar but not identical to the source. Previous work on covariate shift focuses on matching the marginal distributions on observations X across domains while assuming the conditional dis...
متن کاملChallenges Faced by Digital Immigrant Nurse Educators in Adopting Flexible Learning Options during the COVID-19 Pandemic: A Phenomenological Study
Background: The COVID-19 pandemic has forced many educational institutions to shift to flexible learning options (FLO), which require technology in teaching. Digital immigrant nurse educators, who are educators born before 1985, may need help adopting FLO due to their limited knowledge and skills in technology. This study aims to explore the experiences of digital immigrant nurse educators' cha...
متن کاملAn Analysis of Families and Community’s Involvement in The Schools’ Educational and Administrative Processes Using “Overlapping Spheres of Influence” Model
Abstract The overall purpose of this research was to analyze the Families and Community’s involvement in the educational and managerial processes of schools using The “Overlapping Spheres of Influence” Theoretical Model. The research method was an explanatory (quantitative - qualitative) combination. The statistical population consisted of all teachers, administrators and parents of elementary...
متن کامل