Method and computer program product for producing a pattern recognition training set
First Claim
1. A method for generating a set of X training samples from a single ideal pattern for each output class of a pattern recognition classifier, comprising:
- generating a system equivalent pattern for each of a plurality of classes from a corresponding ideal pattern; and
applying a noise model, simulating at least one type of noise expected in a real-world classifier input pattern, to each system equivalent pattern X times to produce, for each output class, X training samples, each simulating defects expected in real-world classifier input patterns.
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Abstract
The present invention recites a method and computer program product for generating a set of training samples from a single ideal pattern for each output class of a pattern recognition classifier. A system equivalent pattern is generated for each of a plurality of classes from a corresponding ideal pattern. A noise model, simulating at least one type of noise expected in a real-world classifier input pattern, is then applied to each system equivalent pattern a set number times to produce, for each output class, a number of training samples. Each training sample simulates defects expected in real-world classifier input patterns.
43 Citations
20 Claims
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1. A method for generating a set of X training samples from a single ideal pattern for each output class of a pattern recognition classifier, comprising:
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generating a system equivalent pattern for each of a plurality of classes from a corresponding ideal pattern; and
applying a noise model, simulating at least one type of noise expected in a real-world classifier input pattern, to each system equivalent pattern X times to produce, for each output class, X training samples, each simulating defects expected in real-world classifier input patterns. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer program product, operative in a data processing system, for generating a set of X training samples from a single ideal pattern for each output class of a pattern recognition classifier, comprising:
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a classifier system simulator that generates a system equivalent pattern for each of a plurality of classes from a corresponding ideal pattern; and
a noise model that simulates at least one type of noise expected in a real-world classifier input pattern and incorporates the simulated noise into each system equivalent pattern X times to produce, for each output class, X training samples, each simulating defects expected in real-world classifier input patterns. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification