2019
Autores
Fernandes, K; Cardoso, JS;
Publicação
NEURAL COMPUTING & APPLICATIONS
Abstract
Transfer learning focuses on building better predictive models by exploiting knowledge gained in previous related tasks, being able to soften the traditional supervised learning assumption of having identical train-test distributions. Most efforts on transfer learning consider revisiting the data from the source tasks or rely on transferring knowledge for specific models. In this paper, a general framework is proposed for transferring knowledge by including a regularization factor based on the structural model similarity between related tasks. The proposed approach is instantiated to different models for regression, classification, ranking and recommender systems, obtaining competitive results in all of them. Also, we explore high-level concepts in transfer learning like sparse transfer, partially observable transfer and cross-model transfer.
2012
Autores
Fernández, K; González, G; Chang, C;
Publicação
Motion in Games - Lecture Notes in Computer Science
Abstract
2012
Autores
Fernandez, K; Chang, C;
Publicação
Artificial Neural Networks in Pattern Recognition - Lecture Notes in Computer Science
Abstract
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