Self-optimizing classifier
First Claim
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1. A method for determining an optimal classifier model for a pattern recognition system, comprising:
- selecting a plurality of initial classifier models from a set of available classifier models;
producing an optimal representative classifier for each classifier model according to an iterative optimization routine; and
accepting the representative classifier having a best associated value for a fitness function.
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Abstract
A method and computer program product are disclosed for determining an optimal classifier model for a pattern recognition system and updating the determined model to recognize new output classes. An initial plurality of classifier models are selected from a set of generated classifier models. An optimal representative classifier for each classifier model is selected according to an iterative optimization routine. The representative classifier having the highest associated value for a fitness function is accepted.
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Citations
21 Claims
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1. A method for determining an optimal classifier model for a pattern recognition system, comprising:
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selecting a plurality of initial classifier models from a set of available classifier models;
producing an optimal representative classifier for each classifier model according to an iterative optimization routine; and
accepting the representative classifier having a best associated value for a fitness function. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method of automatically updating a pattern recognition classifier to recognize additional classes, comprising:
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grouping a plurality of rejected pattern samples into clusters according to similarities among the pattern samples;
searching at least one database to identify at least one of the clusters of rejected pattern samples;
submitting at least one cluster not identified in the database search to an expert verifier for identification if one or more clusters are not identified in the database search; and
adding the pattern samples from the identified clusters to a set of training samples. - View Dependent Claims (8, 9, 10, 11, 14, 15, 16)
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12. A computer program product, operative in a data processing system, for determining an optimal classifier model for a pattern recognition system, comprising:
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a system control program that selects a plurality of classifier models from a set of available classifier models; and
an iterative loop that produces an optimal representative classifier for each classifier model according to an iterative optimization routine;
wherein the system control program accepts the representative classifier having a best associated value for a fitness function upon termination of the iterative optimization routine. - View Dependent Claims (13)
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17. A computer program product, operative in a data processing system, for updating a pattern recognition classifier to recognize new classes, comprising:
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a clustering portion that groups a plurality of rejected pattern samples into clusters according to the similarities between the pattern samples;
an artificially intelligent trained program that searches at least one database to identify at least one of the clusters of rejected pattern samples; and
a controller program that submits at least one cluster not identified in the database search to an expert verifier for identification, if one or more clusters are not identified in the database search, and adds the pattern samples from the identified clusters to a set of training samples. - View Dependent Claims (18, 19, 20, 21)
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Specification