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相关概念视频

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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ALR-HT:一个快速高效的拉索回归,没有超参数调.

Yuhang Wang1, Bin Zou1, Jie Xu2

  • 1Faculty of Mathematics and Statistics, Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430062, China.

Neural networks : the official journal of the International Neural Network Society
|November 15, 2024
PubMed
概括

我们介绍了没有超参数调 (ALR-HT) 的附加拉索回归,这是一种用于高维数据的新方法. 与现有算法相比,ALR-HT提供了更好的性能和更短的时间.

关键词:
添加物模型是一种添加物模型.有关一般化的束.超参数调整 超参数调整拉索回归是一种回归方式.马尔科夫重新采样坡回归的回归方法

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科学领域:

  • 机器学习 机器学习
  • 统计建模 统计建模
  • 数据科学数据科学数据科学

背景情况:

  • 拉索回归对于高维数据和特征选择是有效的.
  • 拉索回归中的超参数调整可能耗时且对杂数据敏感,特别是在大数据环境中.

研究的目的:

  • 引入一种新的添加式拉索回归方法,消除了对超参数调整的需求.
  • 分析拟议方法的概括界限和学习率.
  • 为了证明算法的有效性和多功能性在规范化回归.

主要方法:

  • 将马尔科夫重抽样与添加模型集成在一起,以创建没有超参数调整 (ALR-HT) 的添加式拉索回归.
  • 对ALR-HT的概括边界的估计和快速学习率的建立.
  • 对基准数据集进行实验评估,将ALR-HT与其他算法进行比较.

主要成果:

  • 在采样和训练时间方面,ALR-HT表现出卓越的性能.
  • 与现有算法相比,拟议的方法实现了较低的平均平方误差 (MSE).
  • 当应用到Ridge回归时,ALR-HT显示了多功能性和有效性.

结论:

  • 开发的ALR-HT算法为高维回归任务提供了高效和有效的替代方案.
  • ALR-HT克服了传统拉索回归在超参数调整和噪声数据方面的局限性.
  • 该方法的适应性扩展到其他规范化回归技术.