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PATTERN RECOGNITION
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SHAPER
is a pattern recognition engine that enables software applications to recognize noisy, ill-defined patterns with
great accuracy and speed. |
| As a benchmark the recognition of handwritten symbols
is a difficult classification task. With 30 distinct classes of symbols, 20 training examples
in each symbol class, where each symbol example contains 300 bytes of information, handwriting
recognition performance on a 300 Mhz PC offers: |
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The creation of primitives takes 1 hour. Subsequent training takes 5 minutes. |
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Recognition speed is 0.001 seconds per symbol. |
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Accuracy is greater than 98% on untrained examples and 99.9% on trained examples. |
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The engine uses 30k bytes of memory. |
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The accuracy increases considerably as the number of examples in each symbol class increases.
When there are 200 examples in each class, the accuracy on untrained examples is greater than 99%. Unlike other
recognition algorithms, the training time increases linearly as a function of the size of the
training space. |
SHAPER is substantially more accurate at distinguishing gene expression patterns
in microarray data than handwriting patterns. |
