用向量关系替代权重更新，让表格数据预测更可解释且高效
Learning based on neurovectors for tabular data: a new neural network approach
- 以向量空间结构编码数据，靠能量传播驱动学习而非反向传播
- 在UCI和Kaggle数据集上表现接近传统模型，分类与回归均有效
- 适合追求模型可解释性与动态表征的表格数据场景
本文提出基于神经向量（Neurovectors）的新学习方法，这是一种通过互联节点与向量关系结构化信息的创新范式，用于处理表格数据。不同于依赖反向传播调整权重的传统神经网络，神经向量通过向量空间中的能量传播驱动学习过程，实现更灵活且可解释的学习机制。该方法通过动态生成知识表征，提升了预测模型的可解释性与效率。实验在来自UCI机器学习仓库和Kaggle的多个公开数据集上进行，涵盖分类与回归任务。与标准机器学习及深度学习模型对比表明，神经向量在性能上具有竞争力。
原文摘要 · Abstract (English)
In this paper, we present a novel learning approach based on Neurovectors, an innovative paradigm that structures information through interconnected nodes and vector relationships for tabular data processing. Unlike traditional artificial neural networks that rely on weight adjustment through backpropagation, Neurovectors encode information by structuring data in vector spaces where energy propagation, rather than traditional weight updates, drives the learning process, enabling a more adaptable and explainable learning process. Our method generates dynamic representations of knowledge through neurovectors, thereby improving both the interpretability and efficiency of the predictive model. Experimental results using datasets from well-established repositories such as the UCI machine learning repository and Kaggle are reported both for classification and regression. To evaluate its performance, we compare our approach with standard machine learning and deep learning models, showing that Neurovectors achieve competitive accuracy.
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