New Bootstrap Applications in Supervised Learning

Some bootstrap and boosting methods for problems related to classification are introduced in this article. The first method chooses better boosting weights by using a bootstrap search algorithm. The second method is a good way to define a classification frontier. A new formulation for boosting in linear discriminant analysis is given. Since in this new formulation the uncertainty is represented by the weighted covariance matrix, it is more appropriate from the conceptual point of view. Simulation results show that the proposed methods perform well in data analysis.     

阻尼元件、质量块在力学模型中的等效处理

为了方便求取力学系统模型的传递函数，将力学模型中的阻尼元件与质量块等效成复域内的弹性元件，提出了一个与电网络系统中复阻抗相似的复弹性刚度的新概念。将力学模型中的不同元件模拟成一系列串、并联的弹性元件，用同一概念处理遵循不同物理学定律的元件。避免了在求力学模型系统传递函数的微分方程的列写，而直接画出系统的方块图或信号流图。通过典型力学模型实例说明这一解决问题方法的正确性、简捷性