Abstract
The author consider a binary classification problem with a feature vector of high dimensionality. Spam mail filters are a popular example hereof. A Bayesian approach requires us to estimate the probability of a feature vector given the class of the object. Due to the size of the feature vector this is an unfeasible task. A useful approach is to split the feature space into several (conditionally) independent subspaces. This results in a new problem, namely how to find the ldquobestrdquo subdivision. In this paper the author consider a weighing approach that will perform (asymptotically) as good as the best subdivision and still has a manageable complexity.
| Original language | English |
|---|---|
| Title of host publication | Information Theory and Applications Workshop, 2008 , 3rd ,27 January -1 February 2008, San Diego, U.S.A. |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 1-11 |
| ISBN (Print) | 978-1-4244-2670-6 |
| DOIs | |
| Publication status | Published - 2008 |
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Cite this
Tjalkens, T. J. (2008). Prediction and modeling with partial dependencies. In Information Theory and Applications Workshop, 2008 , 3rd ,27 January -1 February 2008, San Diego, U.S.A. (pp. 1-11). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITA.2008.4601082