Distance Based Active Learning for Domain Adaptation

Christian Pölitz


We investigate methods to apply Domain Adaptation coupled with Active Learning to reduce the number of labels needed to train a classifier. We assume to have a classification task on a given unlabelled set of documents and access to labels from different documents of other sets. The documents from the other sets come from different distributions. Our approach uses Domain Adaptation together with Active Learning to find a minimum number of labelled documents from the different sets to train a high quality classifier. We assume that documents from different sets that are close in a latent topic space can be used for a classification task on a given different set of documents.


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Paper Citation

in Harvard Style

Pölitz C. (2015). Distance Based Active Learning for Domain Adaptation . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 296-303. DOI: 10.5220/0005217302960303

in Bibtex Style

author={Christian Pölitz},
title={Distance Based Active Learning for Domain Adaptation},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Distance Based Active Learning for Domain Adaptation
SN - 978-989-758-076-5
AU - Pölitz C.
PY - 2015
SP - 296
EP - 303
DO - 10.5220/0005217302960303