Probability estimate for K-nearest neighbor
First Claim
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1. A computer-implemented pattern recognition system comprising:
- a classifier that receives pattern-related indicia and classifies the indicia by determining distances of K nearest neighbors; and
a trained probability transducer that calculates posterior probabilities of classes based on the distances of the K nearest neighbor outputs of the classifier.
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Abstract
Systems and methods are disclosed that facilitate producing probabilistic outputs also referred to as posterior probabilities. The probabilistic outputs include an estimate of classification strength. The present invention intercepts non-probabilistic classifier output and applies a set of kernel models based on a softmax function to derive the desired probabilistic outputs. Such probabilistic outputs can be employed with handwriting recognition where the probability of a handwriting sample classification is combined with language models to make better classification decisions.
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Citations
22 Claims
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1. A computer-implemented pattern recognition system comprising:
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a classifier that receives pattern-related indicia and classifies the indicia by determining distances of K nearest neighbors; and a trained probability transducer that calculates posterior probabilities of classes based on the distances of the K nearest neighbor outputs of the classifier. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer-implemented method that generates posterior probabilities comprising:
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computing a non-probabilistic classifier output for a data point, the non-probabilistic classifier output comprising a ranked vector of K nearest neighbor outputs; and computing probabilistic outputs for the data point from the ranked vector of K nearest neighbor outputs utilizing a trained parametric model. - View Dependent Claims (21)
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22. A computer-implemented classification system comprising:
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means for computing a non-probabilistic classifier output for at least one data point, the non-probabilistic classifier output comprising a ranked vector of K nearest neighbor outputs; and means for computing probabilistic outputs for the at least one data point from the ranked vector of K nearest neighbor outputs utilizing a trained parametric model.
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Specification