Hybrid neural network generation system and method
First Claim
1. An artificial neural network that predicts at least one target based upon observations defined in a state space, comprising:
- a first stage that contains a first activation function type, wherein the first stage is predictive of the target, wherein residuals result from predictions by the first stage of the target; and
a second stage that contains a second activation function type, wherein the second stage is predictive of the residuals resulting from the predictions by the first stage;
wherein the first activation function type is a different function type than the second activation function type;
whereby the second activation function type in the second stage provides greater accuracy in predicting the target than if the first activation function type were used in the second stage to provide predictions with respect to the target.
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Abstract
A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.
24 Citations
22 Claims
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1. An artificial neural network that predicts at least one target based upon observations defined in a state space, comprising:
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a first stage that contains a first activation function type, wherein the first stage is predictive of the target, wherein residuals result from predictions by the first stage of the target; and a second stage that contains a second activation function type, wherein the second stage is predictive of the residuals resulting from the predictions by the first stage; wherein the first activation function type is a different function type than the second activation function type;
whereby the second activation function type in the second stage provides greater accuracy in predicting the target than if the first activation function type were used in the second stage to provide predictions with respect to the target. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A method for using an artificial neural network that predicts at least one target based upon observations defined in a state space, comprising:
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using a first stage that contains a first activation function type, wherein the first stage is predictive of the target, wherein residuals result from predictions by the first stage of the target; and using a second stage that contains a second activation function type, wherein the second stage is predictive of the residuals resulting from the predictions by the first stage; wherein the first activation function type is a different function type than the second activation function type;
whereby the second activation function type in the second stage provides greater accuracy in predicting the target than if the first activation function type were used in the second stage to provide predictions with respect to the target.
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22. A computer-readable medium containing computer software comprising program code for carrying out a method for using an artificial neural network that predicts at least one target based upon observations defined in a state space, said method comprising:
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using a first stage that contains a first activation function type, wherein the first stage is predictive of the target, wherein residuals result from predictions by the first stage of the target; and using a second stage that contains a second activation function type, wherein the second stage is predictive of the residuals resulting from the predictions by the first stage; wherein the first activation function type is a different function type than the second activation function type;
whereby the second activation function type in the second stage provides greater accuracy in predicting the target than if the first activation function type were used in the second stage to provide predictions with respect to the target.
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Specification