ADAPTING A PARAMETERIZED CLASSIFIER TO AN ENVIRONMENT
First Claim
1. One or more computer-readable storage media comprising executable instructions to perform a method, the method comprising:
- classifying a first input item based on a first set of parameters that have been determined based on minimization of a first cost function over a set of first examples;
receiving a second example;
calculating a second set of parameters that minimizes a second cost function over said set of first examples and said second example, said second cost function being based on a third cost function that is based (a) on said first cost function, or (b) on a representation of said set of first examples that is smaller in data size than said set of first examples;
generating a label based on a second input item and on said second set of parameters; and
performing an action based on said label.
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Accused Products
Abstract
A classifier is trained on a first set of examples, and the trained classifier is adapted to perform on a second set of examples. The classifier implements a parameterized labeling function. Initial training of the classifier optimizes the labeling function'"'"'s parameters to minimize a cost function. The classifier and its parameters are provided to an environment in which it will operate, along with an approximation function that approximates the cost function using a compact representation of the first set of examples in place of the actual first set. A second set of examples is collected, and the parameters are modified to minimize a combined cost of labeling the first and second sets of examples. The part of the combined cost that represents the cost of the modified parameters applied to the first set is calculated using the approximation function.
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Citations
20 Claims
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1. One or more computer-readable storage media comprising executable instructions to perform a method, the method comprising:
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classifying a first input item based on a first set of parameters that have been determined based on minimization of a first cost function over a set of first examples; receiving a second example; calculating a second set of parameters that minimizes a second cost function over said set of first examples and said second example, said second cost function being based on a third cost function that is based (a) on said first cost function, or (b) on a representation of said set of first examples that is smaller in data size than said set of first examples; generating a label based on a second input item and on said second set of parameters; and performing an action based on said label. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method providing a classifier, the method comprising:
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calculating a first set of parameters that, when used with the classifier to label a first set of examples, minimizes a first cost function over said first set of examples; creating a second cost function that is based on said first cost function and a representation of said first set of examples that is smaller than said first set of examples; and delivering the classifier, said second cost function, a component that minimizes a weighted sum involving said second cost function, and said representation, to an environment in which the classifier will operate. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A system to classify input, the system comprising:
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a camera that collects an input item; a data remembrance component that stores a first set of parameters; one or more components that receive said input item, that generate a label of said input item based on said first set of parameters, and that generate a second set of parameters by minimizing a cost function that is based on a representation of a first set of examples from which said first set of parameters is derived, said representation being smaller in data size than said first set of examples; and an output device through which a result based on said label is communicated to a person. - View Dependent Claims (17, 18, 19, 20)
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Specification