Approximating optimally discrete probability distribution with kth-order dependency for combining multiple decisions

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A probabilistic combination of K classifiers' decisions obtained from samples needs a (K + 1)st-order probability distribution. Chow and Liu (1968) as well as Lewis (1959) proposed an approximation scheme of such a high-order distribution with a product of only first-order tree dependencies. However, if a classifier follows more than two classifiers, such first-order dependency does not estimate adequately a high-order distribution. Therefore, a new method is proposed to approximate optimally the (K + 1)st-order distribution with a product set of kth-order dependencies where 1 less than or equal to k less than or equal to K, which are identified by a systematic dependency-directed approach. And also, a new method is presented to combine probabilistically multiple decisions with the product set of the kth-order dependencies, using a Bayesian formalism.
Publisher
ELSEVIER SCIENCE BV
Issue Date
1997-04
Language
English
Article Type
Article
Keywords

RECOGNITION

Citation

INFORMATION PROCESSING LETTERS, v.62, no.2, pp.67 - 75

ISSN
0020-0190
URI
http://hdl.handle.net/10203/10351
Appears in Collection
CS-Journal Papers(저널논문)
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