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Inductive transfer, or transfer learning, is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, the abilities acquired while learning to walk presumably apply when one learns to run, and knowledge gained while learning to recognize cars could apply when recognizing trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.

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  • Inductive transfer
  • Apprentissage par transfert
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  • L'apprentissage par transfert (transfer learning en anglais) est l'un des champs de recherche de l'apprentissage automatique qui vise à transférer des connaissances d'une ou plusieurs tâche(s) source(s) vers une ou plusieurs tâche(s) cible(s). Il peut être vu comme la capacité d’un système à reconnaître et appliquer des connaissances et des competences, apprises à partir de tâches antérieures, sur de nouvelles tâches ou domaines partageant des similitudes.
  • Inductive transfer, or transfer learning, is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, the abilities acquired while learning to walk presumably apply when one learns to run, and knowledge gained while learning to recognize cars could apply when recognizing trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.
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  • Inductive transfer, or transfer learning, is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, the abilities acquired while learning to walk presumably apply when one learns to run, and knowledge gained while learning to recognize cars could apply when recognizing trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited. The earliest cited work on transfer in machine learning is attributed to Lorien Pratt who formulated the discriminability-based transfer (DBT) algorithm in 1993. In 1997, the journal Machine Learning published a special issue devoted to Inductive Transfer and by 1998, the field had advanced to include multi-task learning, along with a more formal analysis of its theoretical foundations. Learning to Learn, edited by Sebastian Thrun and Pratt, is a comprehensive overview of the state of the art of inductive transfer at the time of its publication. Inductive transfer has also been applied in cognitive science, with the journal Connection Sciencepublishing a special issue on Reuse of Neural Networks through Transfer in 1996. Notably, scientists have developed algorithms for inductive transfer in Markov logic networks and Bayesian networks. Furthermore, researchers have applied techniques for transfer to problems in text classification, and spam filtering.
  • L'apprentissage par transfert (transfer learning en anglais) est l'un des champs de recherche de l'apprentissage automatique qui vise à transférer des connaissances d'une ou plusieurs tâche(s) source(s) vers une ou plusieurs tâche(s) cible(s). Il peut être vu comme la capacité d’un système à reconnaître et appliquer des connaissances et des competences, apprises à partir de tâches antérieures, sur de nouvelles tâches ou domaines partageant des similitudes.
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  • September 2016
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