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Transfer of learning occurs when people apply information, strategies, and skills they have learned to a new situation or context. Transfer is not a discrete activity, but is rather an integral part of the learning process. Researchers attempt to identify when and how transfer occurs and to offer strategies to improve transfer.
Illustration of transfer learning. Transfer learning ( TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Theoretically, transfer of training is a specific application of the theory of transfer of learning that describes the positive, zero, or negative performance outcomes of a training program. [2] The positive transfer of training-- the increase in job performance attributed to training-- has become the goal of many organizations.
In organizational theory, knowledge transfer is the practical problem of transferring knowledge from one part of the organization to another. Like knowledge management, knowledge transfer seeks to organize, create, capture or distribute knowledge and ensure its availability for future users.
Edward Thorndike. Edward Lee Thorndike (August 31, 1874 – August 9, 1949) was an American psychologist who spent nearly his entire career at Teachers College, Columbia University. His work on comparative psychology and the learning process led to the theory of connectionism and helped lay the scientific foundation for educational psychology.
Learning theory (education) Learning theory describes how students receive, process, and retain knowledge during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a worldview, is acquired or changed and knowledge and skills retained.
Another definition of transformative learning was put forward by Edmund O'Sullivan: [25] Transformative learning involves experiencing a deep, structural shift in the basic premises of thought, feelings, and actions. It is a shift of consciousness that dramatically and irreversibly alters our way of being in the world.
Domain adaptation is the ability to apply an algorithm trained in one or more "source domains" to a different (but related) "target domain". Domain adaptation is a subcategory of transfer learning. In domain adaptation, the source and target domains all have the same feature space (but different distributions); in contrast, transfer learning ...
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