More Consideration for the Perceptron

In this paper, we introduce the gated <PRE_TAG>perceptron</POST_TAG>, an enhancement of the conventional perceptron, which incorporates an additional input computed as the product of the existing inputs. This allows the perceptron to capture non-linear interactions between features, significantly improving its ability to classify and regress on complex datasets. We explore its application in both linear and non-linear regression tasks using the Iris dataset, as well as binary and multi-class <PRE_TAG>classification</POST_TAG> problems, including the PIMA Indian dataset and Breast Cancer Wisconsin dataset. Our results demonstrate that the gated <PRE_TAG>perceptron</POST_TAG> can generate more distinct decision regions compared to traditional perceptrons, enhancing its classification capabilities, particularly in handling non-linear data. Performance comparisons show that the gated <PRE_TAG>perceptron</POST_TAG> competes with state-of-the-art classifiers while maintaining a simple architecture.