LASS(Least Absolute Shrinkage and Selection Operator)是一种用于特征选择的方法,也被称为Lasso回归。这种方法在统计学和机器学习中被广泛使用,用于减少模型中的特征数量,同时保持模型的预测能力。LASS方法通过最小化模型中所有特征的绝对值之和来工作,同时保留一些重要的特征,从而实现了特征选择。
1. Linear and Locally Smooth Approximation (LASS)
2. Least Absolute Shrinkage Selection Operator (LASSP)
3. Least Squares Smoothing (LASSO)
4. Least Squares Smoothing Splines (LASSS)
5. Least Absolute Deviations (LAD)
6. Least Absolute Deviation Classification (LAD-C)
7. Least Mean Squares (LMSM)
8. Least Mean Square Classification (LMSM-C)
9. Least Squares Classification (LSC)