Multilayer feedforward networks are universal approximators
Neural Networks
Universal approximation using radial-basis-function networks
Neural Computation
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Binary Rule Generation via Hamming Clustering
IEEE Transactions on Knowledge and Data Engineering
A Note on the Universal Approximation Capability of Support Vector Machines
Neural Processing Letters
Switching neural networks: a new connectionist model for classification
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Switching Neural Network: An application to Regression Problems
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Switching neural networks: a new connectionist model for classification
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Journal of Mathematical Imaging and Vision
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The universal approximation property is an important characteristic of models employed in the solution of machine learning problems. The possibility of approximating within a desired precision any Borel measurable function guarantees the generality of the considered approach. The properties of the class of positive Boolean functions, realizable by digital circuits containing only and and or ports, is examined by considering a proper coding for ordered and nominal variables, which is able to preserve ordering and distance. In particular, it is shown that positive Boolean functions are universal approximators and can therefore be used in the solution of classification and regression problems.