Probability estimate for K-nearest neighbor
First Claim
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1. A method of training a K nearest neighbor classifier, comprising:
- obtaining a set of data comprising a first subset of training data and a second subset of training data;
training the K nearest neighbor classifier on the first subset of training data;
sequentially processing the second subset of training data to compute K nearest neighbor classifier outputs for respective points of the second set of training data; and
determining parameters for a parametric model according to the K nearest neighbor classifier outputs, and true outputs of respective points of the second set of training data.
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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
20 Claims
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1. A method of training a K nearest neighbor classifier, comprising:
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obtaining a set of data comprising a first subset of training data and a second subset of training data;
training the K nearest neighbor classifier on the first subset of training data;
sequentially processing the second subset of training data to compute K nearest neighbor classifier outputs for respective points of the second set of training data; and
determining parameters for a parametric model according to the K nearest neighbor classifier outputs, and true outputs of respective points of the second set of training data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A computer-implemented method of training a K nearest neighbor classifier, comprising:
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obtaining a set of data comprising a first subset of training data and a second subset of training data;
training the K nearest neighbor classifier on the first subset of training data;
sequentially processing the second subset of training data to compute K nearest neighbor classifier outputs for respective points of the second set of training data; and
determining parameters for a parametric model according to the K nearest neighbor classifier outputs, and true outputs of respective points of the second set of training data. - View Dependent Claims (19, 20)
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Specification