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Rooted in this framework, an automotive industry in-situ experimental setup for studying learning, continuous evaluation and knowledge generation in product development is proposed and discussed.
According to [17], boosting algorithm has its roots in a theoretical framework for studying machine learning called probably approximately correct (PAC) learning model [18], regarding learning as a phenomenon of knowledge acquisition in the absence of explicit programming.
Formal learning theory provides a framework for studying the long-run consequences of a wide range of methodologies.
The design phase composes of 5 steps as follows: 1) Studying and analyzing the related principles and theories, 2) Studying the context of learning environments, 3) Designing a framework for cooperative learning through web using data mining technique to group learners, 4) Assessing the framework by six experts and 5) Improving the framework.
Fig. 1 Graphic of conceptual framework for STEM learning.
We propose parallel active-set algorithms to optimize the sparse coding models, and propose a generic framework for dictionary learning.
Much more work has been done in robot learning, e.g., Knox et al. [69] presented a case study of applying a framework for learning from human feedback to a physically embodied robot.
We draw on our studies of video-conferenced classrooms, group discussion spaces, and online communities to suggest a framework for understanding how learning networks can benefit from various e-learning venues.
We have proposed a framework for learning in clinical settings which extends current models.
Soh [ 15] describes a framework for distributed ontology learning embedded in a multiagent environment.
A framework for the study of learning, and some essential issues in experimental design, are discussed.
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