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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.
Knowledge transfer refers to transferring an awareness of facts or practical skills from one entity to another. The particular profile of transfer processes activated for a given situation depends on (a) the type of knowledge to be transferred and how it is represented (the source and recipient relationship with this knowledge) and (b) the processing demands of the transfer task.
Convolutional neural network ( CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections.
Learning. 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 ...
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 ...
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 ...
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