OASIS: Online Active Semi-Supervised Learning

Authors

  • Andrew Goldberg Arcode Corporation
  • Xiaojin Zhu University of Wisconsin-Madison
  • Alex Furger University of Wisconsin-Madison
  • Jun-Ming Xu University of Wisconsin-Madison

Abstract

We consider a learning setting of importance to large scale machine learning: potentially unlimited data arrives sequentially, but only a small fraction of it is labeled. The learner cannot store the data; it should learn from both labeled and unlabeled data, and it may also request labels for some of the unlabeled items. This setting is frequently encountered in real-world applications and has the characteristics of online, semi-supervised, and active learning. Yet previous learning models fail to consider these characteristics jointly. We present OASIS, a Bayesian model for this learning setting. The main contributions of the model include the novel integration of a semi-supervised likelihood function, a sequential Monte Carlo scheme for efficient online Bayesian updating, and a posterior-reduction criterion for active learning. Encouraging results on both synthetic and real-world optical character recognition data demonstrate the synergy of these characteristics in OASIS.

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Published

2011-08-04

How to Cite

Goldberg, A., Zhu, X., Furger, A., & Xu, J.-M. (2011). OASIS: Online Active Semi-Supervised Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 362-367. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7910

Issue

Section

AAAI Technical Track: Machine Learning