Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network - Archive ouverte HAL
Proceedings/Recueil Des Communications Proceedings of Machine Learning Research Année : 2023

Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network

Résumé

This paper proposes an understandable neural network whose score function is modeled as an additive sum of univariate spline functions. It extends usual understandable models like generative additive models, spline-based models, and neural additive models. It is shown that this neural network can be approximated by a logistic regression whose inputs are obtained with a non-linear preprocessing of input data. This preprocessing depends on the neural network initialization but this paper establishes that it can be replaced by a non random kernel-based preprocessing that no longer depends on the initialization. Hence, the convergence of the training process is guaranteed and the solution is unique for a given training dataset.
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Dates et versions

hal-04195975 , version 1 (05-09-2023)

Identifiants

  • HAL Id : hal-04195975 , version 1

Citer

Marie Guyomard, Susana Barbosa, Lionel Fillatre. Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network. Proceedings of Machine Learning Research, 202, pp.12268-12291, 2023. ⟨hal-04195975⟩
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