改进感知机通过特征乘积捕捉非线性关系，提升分类回归能力。
More Consideration for the Perceptron
- 引入门控输入，由原特征乘积生成，增强非线性建模能力。
- 在鸢尾花、乳腺癌等数据集上，决策边界更清晰，分类性能显著提升。
- 结构简单却媲美先进模型，适合需要轻量级非线性处理的场景。
本文提出门控感知机（gated perceptron），在传统感知机基础上引入一个由现有输入乘积生成的额外输入，使其能够捕捉特征间的非线性交互，显著提升对复杂数据集的分类与回归能力。我们在鸢尾花（Iris）数据集上测试其在线性与非线性回归任务中的表现，并在印度糖尿病（PIMA Indian）、威斯康星乳腺癌（Breast Cancer Wisconsin）数据集上评估其二分类与多分类性能。结果表明，门控感知机生成的决策区域更具区分性，尤其在处理非线性数据时分类能力明显优于传统感知机。性能对比显示，其表现可与当前主流分类器媲美，同时保持简洁架构。
原文摘要 · Abstract (English)
In this paper, we introduce the gated perceptron, 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 classification problems, including the PIMA Indian dataset and Breast Cancer Wisconsin dataset. Our results demonstrate that the gated perceptron 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 perceptron competes with state-of-the-art classifiers while maintaining a simple architecture.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。