AI Research
Regularized Least Squares Training of Quadratic Neural Networks with Applications to System Identification
Advancing neural network optimization through robust quadratic modeling and least squares regularization
This paper proposes a least squares approach for the training of quadratic neural networks with regularization. The proposed methodology yields a lower bound on the solution of the training optimization problem for the case where the regularization coefficient is positive. Moreov...
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ArXiv CS.LG
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ArXiv CS.LG
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This research introduces a novel mathematical framework that streamlines the training of quadratic neural networks using regularized least squares. By establishing lower bounds for optimization, it provides a more predictable and stable pathway for complex system identification tasks.
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