Minimally-Supervised Classification using Multiple Observation Sets

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Minimally-Supervised Classification using Multiple Observation Sets
This paper discusses building complex classifiers from a single labeled example and vast number of unlabeled observation sets, each derived from observation of a single process or object. When data can be measured by observation, it is often plentiful and it is often possible to make more than one observation of the state of a process or object. This paper discusses how to exploit the variability across such sets of observations of the same object to estimate class labels for unlabeled examples given a minimal number of labeled examples. In contrast to similar semi-supervised classification procedures that define the likelihood that two observations share a label as a function of the embedded distance between the two observations, this method uses the Naive Bayes estimate of how often the two observations did result from the same observed process. Exploiting this additional source of information in an iterative estimation procedure can generalize complex classification models from sin...
Chris Stauffer
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2003
Where ICCV
Authors Chris Stauffer
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