A Bayesian Approach to Semi-Supervised Learning

Recent research in automated learning has focused on algorithms that learn from a combination of tagged and untagged data. Such algorithms can be referred to as semi-supervised in contrast to unsupervised, which refers to algorithms requiring no tagged data whatsoever. This paper presents a Bayesian approach to semi-supervised learning. In this approach, the parameters of a probability model are estimated using Bayesian techniques and then used to perform classification. The prior probability distribution is formulated from the tagged data via a process akin to stochastic generalization. Intuitively, the generalization process starts with a small amount of tagged data and adds to it new pseudo-counts that are similar to those that would be expected in a larger data sample from the same population. The prior distribution together with the untagged data form the posterior distribution which is used to estimate the model parameters via the EM algorithm. This procedure is demonstrated by...

• Type: Conference
• Year: 2001
• Where: NLPRS
• Authors: Rebecca F. Bruce
• Keywords: Natural Language Processing, NLPRS 2001, Prior Probability Distribution, Tagged Data, Untagged Data