A Comparative Study of Methods for Transductive Transfer Learning
Andrew Arnold,Ramesh Nallapati,William W. Cohen +2 more
- pp 77-82
TLDR
A novel maximum entropy based technique, iterative feature transformation (IFT), is introduced and it is shown how simple relaxations, such as providing additional information like the proportion of positive examples in the test data, can significantly improve the performance of some of the transductive transfer learners.Abstract:Â
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While previous work has studied the supervised version of this problem, we study the more challenging case of unsupervised transductive transfer learning, where no labeled data from the target domain are available at training. We describe some current state-of-the-art inductive and transductive approaches and then adapt these models to the problem of transfer learning for protein name extraction. In the process, we introduce a novel maximum entropy based technique, iterative feature transformation (IFT), and show that it achieves comparable performance with state-of-the-art transductive SVMs. We also show how simple relaxations, such as providing additional information like the proportion of positive examples in the test data, can significantly improve the performance of some of the transductive transfer learners.read more
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References
More filters
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings Article
A comparison of event models for naive bayes text classification
Andrew McCallum,Kamal Nigam +1 more
TL;DR: It is found that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes--providing on average a 27% reduction in error over the multi -variateBernoulli model at any vocabulary size.
Journal ArticleDOI
A maximum entropy approach to natural language processing
TL;DR: A maximum-likelihood approach for automatically constructing maximum entropy models is presented and how to implement this approach efficiently is described, using as examples several problems in natural language processing.
Journal ArticleDOI
Text Classification from Labeled and Unlabeled Documents using EM
TL;DR: This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions.