Automatic determination of report granularity
First Claim
1. A method for statistical classification in target recognition, comprising the steps of:
- gathering sets of information representative of features of a target;
creating basic probability assignments based on said sets of information;
determining a coarse information set from elements in a choice set;
performing coarsening on said sets of information;
performing linear superposition on each information source; and
combining information representative of said features to reach a conclusion regarding the target.
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Abstract
The present invention provides a method of performing statistical classification that can resolve conflict in independent sources of information, thereby creating a robust statistical classifier that has superior performance to classifiers currently available. Additionally, the present invention is automatically trainable, yielding improved classification performance. The present invention may be embodied in a method of statistically classifying events or objects, including the steps of gathering sets of information representative of features of an object or event; creating basic probability assignments based on said sets of information; determining a coarse information set from said sets of information; performing coarsening on said sets of information; performing linear superposition on each feature; and combining all features to reach a conclusion.
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Citations
9 Claims
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1. A method for statistical classification in target recognition, comprising the steps of:
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gathering sets of information representative of features of a target; creating basic probability assignments based on said sets of information; determining a coarse information set from elements in a choice set; performing coarsening on said sets of information; performing linear superposition on each information source; and combining information representative of said features to reach a conclusion regarding the target. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for statistical classification to resolve conflicting sets of information about an object, comprising the steps of:
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gathering sets of information representative of features of an object; creating basic probability assignments based on sail sets of information; determining a coarse information set from elements in a choice set; performing coarsening on said sets of information; performing linear superposition on each information source; and combining information representative of said features to reach a conclusion regarding the object.
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Specification