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Transfer of learning. 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 ...
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.
Transfer of learning. Transfer of learning is the idea that what one learns in school somehow carries over to situations different from that particular time and that particular setting. Transfer was amongst the first phenomena tested in educational psychology. Edward Lee Thorndike was a pioneer in transfer research.
For example, after completing a safety course, transfer of training occurs when the employee uses learned safety behaviors in their work environment. [1] 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 ...
e. Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. [1] The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. [2] Some learning is immediate, induced by a single event (e.g ...
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.
Generalization (learning) Generalization is the concept that humans, other animals, and artificial neural networks use past learning in present situations of learning if the conditions in the situations are regarded as similar. [1] The learner uses generalized patterns, principles, and other similarities between past experiences and novel ...
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 ...