SYSTEM AND METHOD FOR LEARNING
First Claim
1. A method used in a computer, comprising:
- receiving training data including attribute data and label information, to create an initial prediction model based on said attribute data and said label information;
calculating, based on said initial prediction model used as a discriminant function, a gradient of a loss function, which is differentiable with respect to said discriminant function and satisfies a monotonous convex function, from said discriminant function and said label information;
creating a prediction model from said attribute data and said gradient while assuming that said gradient is label information of each sample of said training data; and
updating said discriminant function based on said created prediction model.
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Accused Products
Abstract
A method of learning discriminant function for predicting label information by using computer includes: receiving training data including attribute data and label information, to create an initial prediction model based on the attribute data and the label information; calculating, based on the initial prediction model used as a discriminant function, a gradient of a loss function, which is differentiable with respect to the discriminant function and satisfies a monotonous convex function, from the discriminant function and the label information; creating a prediction model from the attribute data and the gradient while assuming that the gradient is label information of each sample of the training data; and updating the discriminant function based on the created prediction model.
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Citations
20 Claims
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1. A method used in a computer, comprising:
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receiving training data including attribute data and label information, to create an initial prediction model based on said attribute data and said label information; calculating, based on said initial prediction model used as a discriminant function, a gradient of a loss function, which is differentiable with respect to said discriminant function and satisfies a monotonous convex function, from said discriminant function and said label information; creating a prediction model from said attribute data and said gradient while assuming that said gradient is label information of each sample of said training data; and updating said discriminant function based on said created prediction model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 12)
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9. A system using a computer, comprising:
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initial-prediction-model creation section that receives training data including attribute data and label information, to create an initial prediction model based on said attribute data and said label information; a gradient calculation section that calculates, based on said initial prediction model used as a discriminant function, a gradient of a loss function, which is differentiable with respect to said discriminant function and satisfies a monotonous convex function, from said discriminant function and said label information; a prediction-model creation section that creates a prediction model from said attribute data and said gradient while assuming that said gradient is label information of each sample of said training data; and an update section that updates said discriminant function based on said created prediction model. - View Dependent Claims (10, 11, 13, 14, 15, 16)
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17. A computer-readable medium encoded with a computer program running on a computer, said computer program causes said computer to:
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receive training data including attribute data and label information, to create an initial prediction model based on said attribute data and said label information; calculate, based on said initial prediction model used as a discriminant function, a gradient of a loss function, which is differentiable with respect to said discriminant function and satisfies a monotonous convex function, from said discriminant function and said label information; create a prediction model from said attribute data and said gradient while assuming that said gradient is label information of each sample of said training data; and update said discriminant function based on said created prediction model. - View Dependent Claims (18, 19, 20)
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Specification