ICCV
2011
IEEE
12 years 4 months ago
2011
IEEE
Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we p...
ECIR
2011
Springer
12 years 7 months ago
2011
Springer
Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance ...
AIPS
2011
12 years 8 months ago
2011
Applying learning techniques to acquire action models is an area of intense research interest. Most previous works in this area have assumed that there is a significant amount of...
ACL
2011
12 years 8 months ago
2011
It is well known that parsing accuracy suffers when a model is applied to out-of-domain data. It is also known that the most beneficial data to parse a given domain is data that ...
ACL
2011
12 years 8 months ago
2011
We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative m...
TNN
2011
12 years 11 months ago
2011
Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature rep...
EMNLP
2009
13 years 2 months ago
2009
Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retr...
EMNLP
2010
13 years 2 months ago
2010
We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to partof-speech (POS) tagging. The algorithm uses a simil...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning ...
EMNLP
2006
13 years 5 months ago
2006
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For...