Transfer Learning and Applications

Abstract : In machine learning and data mining, we often encounter situations where we have an insufficient amount of high-quality data in a target domain, but we may have plenty of auxiliary data in related domains. Transfer learning aims to exploit these additional data to improve the learning performance in the target domain. In this talk, I will give an overview on some recent advances in transfer learning for challenging data mining problems. I will present some theoretical challenges to transfer learning, survey the solutions to them, and discuss several innovative applications of transfer learning, including learning in heterogeneous cross-media domains and in online recommendation, social media and social network mining.
Type de document :
Communication dans un congrès
Zhongzhi Shi; David Leake; Sunil Vadera. 7th International Conference on Intelligent Information Processing (IIP), Oct 2012, Guilin, China. Springer, IFIP Advances in Information and Communication Technology, AICT-385, pp.2-2, 2012, Intelligent Information Processing VI. 〈10.1007/978-3-642-32891-6_2〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01524983
Contributeur : Hal Ifip <>
Soumis le : vendredi 19 mai 2017 - 10:43:38
Dernière modification le : vendredi 19 mai 2017 - 10:45:55

Fichier

978-3-642-32891-6_2_Chapter.pd...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Qiang Yang. Transfer Learning and Applications. Zhongzhi Shi; David Leake; Sunil Vadera. 7th International Conference on Intelligent Information Processing (IIP), Oct 2012, Guilin, China. Springer, IFIP Advances in Information and Communication Technology, AICT-385, pp.2-2, 2012, Intelligent Information Processing VI. 〈10.1007/978-3-642-32891-6_2〉. 〈hal-01524983〉

Partager

Métriques

Consultations de la notice

108

Téléchargements de fichiers

18