← Back to papers
2010 NIPS NeurIPS 2010

Co-regularization Based Semi-supervised Domain Adaptation

Abstract

This paper presents a co-regularization based approach to semi-supervised domain adaptation. Our proposed approach (EA++) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further enable the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement and can be applied as a pre-processing step to any supervised learner. Our theoretical analysis (in terms of Rademacher complexity) of EA and EA++ show that the hypothesis class of EA++ has lower complexity (compared to EA) and hence results in tighter generalization bounds. Experimental results on sentiment analysis tasks reinforce our theoretical findings and demonstrate the efficacy of the proposed method when compared to EA as well as a few other baseline approaches.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer — co-regularization
🐣 Hot Topic Early Bird — semi-supervised learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
📈 Trend Setter — Transfer Learning

Authors

Abhishek Kumar , Avishek Saha , Hal Daume

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Application Areas > Domain Adaptation Machine Learning > Learning Types > Transfer Learning Machine Learning > Learning Types > Domain Adaptation Artificial Intelligence > Learning Paradigms > Domain Adaptation

Keywords

semi-supervised learning transfer learning domain adaptation sentiment analysis rademacher complexity co-regularization semi-supervised domain adaptation co-regularization method
Download PDF

Related papers

Link Discovery using Graph Feature Tracking 2010
Trading off Mistakes and Don't-Know Predictions 2010
A Novel Kernel for Learning a Neuron Model from Spike Train Data 2010
Decomposing Isotonic Regression for Efficiently Solving Large Problems 2010
Learning Kernels with Radiuses of Minimum Enclosing Balls 2010