Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond.

ABSTRACT: In a seminal book, Minsky and Papert define the perceptron as a limited implementation of what they called "parallel machines." They showed that some binary Boolean functions including XOR are not definable in a single layer perceptron due to its limited capacity to learn only linearly separable functions. In this work, we propose a new more powerful implementation of such parallel machines. This new mathematical tool is defined using analytic sinusoids-instead of linear combinations-to form an analytic signal representation of the function that we want to learn. We show that this re-formulated parallel mechanism can learn, with a single layer, any non-linear k-ary Boolean function. Finally, to provide an example of its practical applications, we show that it outperforms the single

SUBMITTER: Mendez Lucero MA 

PROVIDER: S-EPMC9203047 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature