Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The generalization performance of feedforward layered perceptrons can, in many cases, be improved either by smoothing the target via convolution, regularizing the training error with a smoothing constraint, decreasing the gain (i.e., slope) of the sigmoid nonlinearities, or adding noise (i.e., jitter) to the input training data, In certain important cases, the results of these procedures yield highly similar results although at different costs. Training with jitter, for example, requires significantly more computation than sigmoid scaling. >
['Russell Reed', 'Robert J. Marks', 'Seho Oh']
I am writing a research paper on a idea Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter
[{"from": "human", "value": "I am writing a research paper on a idea Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The generalization performance of feedforward layered perceptrons can, in many cases, be improved either by smoothing the target via convolution, regularizing the training error with a smoothing constraint, decreasing the gain (i.e., slope) of the sigmoid nonlinearities, or adding noise (i.e., jitter) to the input training data, In certain important cases, the results of these procedures yield highly similar results although at different costs. Training with jitter, for example, requires significantly more computation than sigmoid scaling. >"}]