Reducing the size of a training set for classification
First Claim
1. A method for classifying a data pattern into one or more groups, comprising:
- computing a decision boundary which separates a first group of data patterns in a training data set from a second group of data patterns in the training data set;
for each data pattern in the training data set,determining if removing the data pattern from the training data set substantially affects the resulting decision boundary; and
if so, marking the data pattern as a key pattern;
removing all data patterns that are not marked as key patterns to produce a reduced training data set which represents the decision boundary;
classifying a previously unseen data pattern into one or more groups by applying the decision boundary to the previously unseen data pattern; and
producing a result which indicates the one or more groups of the previously unseen data pattern.
2 Assignments
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Accused Products
Abstract
A system that reduces the size of a design data set. During this design data set reduction operation, the system computes a decision boundary which separates a first group of data patterns in a training data set from a second group of data patterns in the training data set. For each data pattern in the training data set, the system determines if removing the data pattern from the training data set substantially affects the resulting decision boundary. If so, the system marks the data pattern as a key pattern. The system then removes all data patterns that are not marked as key patterns to produce a reduced training data set which represents the decision boundary.
38 Citations
19 Claims
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1. A method for classifying a data pattern into one or more groups, comprising:
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computing a decision boundary which separates a first group of data patterns in a training data set from a second group of data patterns in the training data set; for each data pattern in the training data set, determining if removing the data pattern from the training data set substantially affects the resulting decision boundary; and if so, marking the data pattern as a key pattern; removing all data patterns that are not marked as key patterns to produce a reduced training data set which represents the decision boundary; classifying a previously unseen data pattern into one or more groups by applying the decision boundary to the previously unseen data pattern; and producing a result which indicates the one or more groups of the previously unseen data pattern. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for classifying a data pattern into one or more groups, the method comprising:
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computing a decision boundary which separates a first group of data patterns in a training data set from a second group of data patterns in the training data set; for each data pattern in the training data set, determining if removing the data pattern from the training data set substantially affects the resulting decision boundary; and if so, marking the data pattern as a key pattern; removing all data patterns that are not marked as key patterns to produce a reduced training data set which represents the decision boundary; classifying a previously unseen data pattern into one or more groups by applying the decision boundary to the previously unseen data pattern; and producing a result which indicates the one or more groups of the previously unseen data pattern. - View Dependent Claims (8, 9, 10, 11, 12)
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13. An apparatus that classifies a data pattern into one or more groups comprising:
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a computing mechanism configured to compute a decision boundary which separates a first group of data patterns in a training data set from a second group of data patterns in the training data set; a pattern-reduction mechanism configured to; for each data pattern in the training data set, to determine if removing the data pattern from the training data set substantially affects the resulting decision boundary; and if so, to mark the data pattern as a key pattern; and
toremove all data patterns that are not marked as key patterns to produce a reduced training data set which represents the decision boundary; a classification mechanism configured to classify a previously unseen data pattern into one or more groups by applying the decision boundary to the previously unseen data pattern; and a result-producing mechanism configured to produce a result which indicates the one or more groups of the previously unseen data pattern. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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Specification