Q. Yang. Advances in Machine Learning, Volume 5828 von Lecture Notes in Computer Science, Springer, Berlin / Heidelberg, (2009)
DOI: 10.1007/978-3-642-05224-8_3
Zusammenfassung
Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.
%0 Book Section
%1 springerlink:10.1007/978-3-642-05224-8_3
%A Yang, Qiang
%B Advances in Machine Learning
%C Berlin / Heidelberg
%D 2009
%E Zhou, Zhi-Hua
%E Washio, Takashi
%I Springer
%K cross domain learning-to-rank ranking search transfer-learning
%P 10-22
%R 10.1007/978-3-642-05224-8_3
%T Transfer Learning beyond Text Classification
%U http://dx.doi.org/10.1007/978-3-642-05224-8_3
%V 5828
%X Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.
@incollection{springerlink:10.1007/978-3-642-05224-8_3,
abstract = {Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.},
added-at = {2010-09-24T09:53:48.000+0200},
address = {Berlin / Heidelberg},
affiliation = {Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong},
author = {Yang, Qiang},
biburl = {https://www.bibsonomy.org/bibtex/27925eeb49ac484bd6a3d9e0b4250065c/beate},
booktitle = {Advances in Machine Learning},
description = {SpringerLink - Abstract},
doi = {10.1007/978-3-642-05224-8_3},
editor = {Zhou, Zhi-Hua and Washio, Takashi},
interhash = {d385be63f04a3b80e32b62dd6a07c7ab},
intrahash = {7925eeb49ac484bd6a3d9e0b4250065c},
keywords = {cross domain learning-to-rank ranking search transfer-learning},
pages = {10-22},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2010-09-24T09:53:48.000+0200},
title = {Transfer Learning beyond Text Classification},
url = {http://dx.doi.org/10.1007/978-3-642-05224-8_3},
volume = 5828,
year = 2009
}