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US 2010/0138372 A1Published application
Cognitive pattern matching system with built-in confidence measure
OwnerDouglas A. PALMERinventor
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Abstract
Artificial neural systems are very powerful tools for pattern matching, classification, feature extraction and signal analysis. Systems to date lack an essential feature of their biological counterparts, a measure of confidence that the network response has actually been trained and is not an artifact. In the proposed artificial neural system one output is a produced (trained) measure of confidence in the remaining outputs i.e. a measure of certainty that the inputs match the training data.
Claims1
An artificial neural system comprising:
- a set of neuron models;
- a set of interconnections between the neuron models representing weights, wherein an input subset of the interconnections serve as inputs and an output subset of the interconnections serve as outputs;
- a confidence output dedicated to producing a representation of the confidence in the output subset;
- and
- a processor configured to apply a two-pass learning method to the set of neuron models, the set of interconnections, the input and output subsets, and the confidence output.
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Dependencies are inferred from the claim text.