Optimal cessation of training and assessment of accuracy in a given class of neural networks
DCFirst Claim
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1. A method for configuring an iterative, self-correcting algorithm having an objective function, the method comprising the steps of:
- selecting training data;
iterating the algorithm on the selected training data to modify weights in the algorithm; and
relying on characteristics of the objective function to determine when the solution to the algorithm has been reached.
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Abstract
A system, method, and process for configuring iterative, self-correcting algorithms, such as neural networks, so that the weights or characteristics to which the algorithm converge to do not require the use of test or validation sets, and the maximum error in failing to achieve optimal cessation of training can be calculated. In addition, a method for internally validating the correctness, i.e. determining the degree of accuracy of the predictions derived from the system, method, and process of the present invention is disclosed.
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12 Claims
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1. A method for configuring an iterative, self-correcting algorithm having an objective function, the method comprising the steps of:
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selecting training data; iterating the algorithm on the selected training data to modify weights in the algorithm; and relying on characteristics of the objective function to determine when the solution to the algorithm has been reached. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for configuring an iterative, self-correcting algorithm having an objective function, the method comprising the steps of:
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selecting training data; using back propagation to train the algorithm by iterating the algorithm on the selected training data to modify weights in the algorithm; and relying on characteristics of the objective function to determine when the solution to the algorithm has been reached. - View Dependent Claims (12)
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Specification