Training apparatus and method
First Claim
1. An apparatus for interpreting data, comprising:
- a current first classifier operative to interpret a plurality of actual examples of the data and to output an interpretation of each interpreted example and a certainty value associated with each interpretation wherein the current first classifier comprises a chooser operative to discriminate between certain ones of the outputted interpretations having respective high certainty values and uncertain ones of the outputted interpretations having respective low certainty values and to select and output each of the actual examples associated with a respective uncertain one of the interpretations;
a second classifier operative to annotate each of the interpreted examples associated with the selected uncertain ones of the interpretations and to output a preferred interpretation for each interpreted example associated with the selected uncertain ones of the interpretations; and
an uncertainty measuring device generator operative to produce a next first classifier by utilizing at least one annotated example and its associated preferred interpretation, the next first classifier capable of interpreting subsequent actual examples of the data more accurately than the current first classifier.
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Accused Products
Abstract
Apparatus and methods for training classifiers. The apparatus includes a degree of certainty classifier which classifies examples in categories and indicates a degree of certainty regarding the classification and an annotating classifier which receives classified examples with a low degree of certainty from the degree of certainty classifier and annotates them to indicate whether their classifications are correct. The annotated examples are then used to train another classifier. In one version of the invention, the other classifier is a new version of the degree of certainty classifier, and training continues until the degree of certainty classifier has satisfactory performance. The degree of certainty classifier of the embodiment is a probabilistic binary classifier which is trained using relevance feedback. The annotating classifier may include an interactive interface which permits a human user of the system to examine an example and indicate whether it was properly classified.
130 Citations
15 Claims
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1. An apparatus for interpreting data, comprising:
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a current first classifier operative to interpret a plurality of actual examples of the data and to output an interpretation of each interpreted example and a certainty value associated with each interpretation wherein the current first classifier comprises a chooser operative to discriminate between certain ones of the outputted interpretations having respective high certainty values and uncertain ones of the outputted interpretations having respective low certainty values and to select and output each of the actual examples associated with a respective uncertain one of the interpretations; a second classifier operative to annotate each of the interpreted examples associated with the selected uncertain ones of the interpretations and to output a preferred interpretation for each interpreted example associated with the selected uncertain ones of the interpretations; and an uncertainty measuring device generator operative to produce a next first classifier by utilizing at least one annotated example and its associated preferred interpretation, the next first classifier capable of interpreting subsequent actual examples of the data more accurately than the current first classifier. - View Dependent Claims (2, 3, 8, 9, 10, 11, 12, 15)
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4. An apparatus for interpreting data, comprising:
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a first classifier operative to interpret a plurality of actual examples of the data and to output an interpretation of each interpreted example and a certainty value associated with each interpretation wherein the first classifier comprises a chooser operative to discriminate between certain ones of the outputted interpretations having respective high certainty values and uncertain ones of the outputted interpretations having respective low certainty values and to select and output each of the interpreted examples associated with a respective uncertain one of the interpretations; a second classifier operative to annotate each of the interpreted examples associated with the selected uncertain ones of the interpretations and to output a preferred interpretation for each interpreted example associated with the selected uncertain ones of the interpretations; and an uncertainty measuring device generator operative to produce a next first classifier by using at least one annotated example and its associated preferred interpretation, the next first classifier capable of interpreting subsequent actual examples of the data more accurately than the first classifier. - View Dependent Claims (5, 6)
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7. An apparatus for interpreting data, comprising:
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a first classifier operative to interpret a plurality of actual examples of the data according to a first principle and to output an interpretation of each interpreted example and a certainty value associated with each interpretation wherein the first classifier comprises a chooser operative to discriminate between certain ones of the outputted interpretations having respective high certainty values and uncertain ones of the outputted interpretations having a low certainty values and to select and output each of the interpreted examples associated with a respective uncertain one of the interpretations; a second classifier operative to annotate each of the interpreted examples associated with the selected uncertain ones of the interpretations and to output a preferred interpretation for each interpreted example associated with the selected uncertain ones of the interpretations; and an uncertainty measuring device generator operative to produce a third classifier by utilizing at least one annotated example and its associated preferred interpretation, the third classifier operative to interpret subsequent actual examples of the data according to a second principle different from the first principle. - View Dependent Claims (13, 14)
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Specification