论文标题
影响学习：一种来自特征和竞争的学习方法
Impact Learning: A Learning Method from Features Impact and Competition
论文作者
论文摘要
机器学习是对计算机算法的研究，可以根据数据和经验自动改进。机器学习算法从示例数据（称为培训数据）构建一个模型，以做出预测或判断，而无需明确编程。已经开发了各种众所周知的机器学习算法，用于计算机科学领域以分析数据。本文介绍了一种称为Impact Learning的新机器学习算法。影响学习是一种有监督的学习算法，可以在分类和回归问题中合并。它还可以在分析竞争数据方面表现出其优势。该算法对于从竞争状况中学习而引人注目，竞争来自自主特征的影响。它是由自然增加（RNI）的固有速率的亮点的影响来制备的。此外，我们表现出对传统机器学习算法的影响的影响。
Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm.
