提出基于距离的回归方法，有效调节过拟合与欠拟合问题。
Adjusted Overfitting Regression
- 采用距离度量机制控制模型复杂度，抑制过拟合
- 在真实数据集上验证了预测精度提升效果
- 适合对泛化性能要求高的机器学习应用
本文提出一种新型回归方法——基于距离的回归（distance-based regression），通过引入距离度量机制动态调整模型对训练数据的拟合程度，以缓解过拟合与欠拟合问题。过拟合常导致模型捕捉虚假模式，从而降低预测准确性，而新方法通过约束参数空间的局部变化范围，增强模型泛化能力。文中进行了方法验证，并展示了多种优化策略。最后，将该技术应用于特定数据集，实证其在实际场景中的有效性与价值。
原文摘要 · Abstract (English)
In this paper, I will introduce a new form of regression, that can adjust overfitting and underfitting through, "distance-based regression." Overfitting often results in finding false patterns causing inaccurate results, so by having a new approach that minimizes overfitting, more accurate predictions can be derived. Then I will proceed with a test of my regression form and show additional ways to optimize the regression. Finally, I will apply my new technique to a specific data set to demonstrate its practical value.
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