Abstract
A nonparametric algorithm is presented for the hierarchical partitioning of the feature space. The algorithm is based on the concept of average mutual information, and is suitable for multifeature multicategory pattern recognition problems. The algorithm generates an efficient partitioning tree for specified probability of error by maximizing the amount of average mutual information gain at each partitioning step. A confidence bound expression is presented for the resulting classifier. Three examples, including one of handprinted numeral recognition, are presented to demonstrate the effectiveness of the algorithm.
Keywords
Mutual informationPattern recognition (psychology)Artificial intelligenceClassifier (UML)Computer scienceNumeral systemNonparametric statisticsMathematicsStatistics
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Publication Info
- Year
- 1982
- Type
- article
- Volume
- PAMI-4
- Issue
- 4
- Pages
- 441-445
- Citations
- 182
- Access
- Closed
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Cite This
Ishwar K. Sethi,
G. P. R. Sarvarayudu
(1982).
Hierarchical Classifier Design Using Mutual Information.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, PAMI-4
(4)
, 441-445.
https://doi.org/10.1109/tpami.1982.4767278
Identifiers
- DOI
- 10.1109/tpami.1982.4767278