p-Adic Polynomial Regression as Alternative to Neural Network for Approximating p-Adic Functions of Many Variables
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Abstract (arXiv)
A method for approximating continuous functions mathbbZpnrightarrowmathbbZp by a linear superposition of continuous functions mathbbZprightarrowmathbbZp is presented and a polynomial regression model is constructed that allows approximating such functions with any degree of accuracy. A physical interpretation of such a model is given and possible methods for its training are discussed. The proposed model can be considered as a simple alternative to possible p-adic models based on neural network architecture.
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