Development and Evaluation of Robust Statistical Method for Adaptive Handwritten Character Recognition System

作为基于决策树的非参数分类方法，CART和C4。五是典型。在本研究中，我们将以决策树为基础的非参数方法导入典型判别分析。我们称之为基于决策树的方法，已被导入到典型判别分析CDAT（典型判别分析树）。在CDAT中，将区域划分为两个的改善指数定义为组间方差-协方差矩阵与组内方差-协方差矩阵的特征值之和在划分前与划分后之差。利用C语言在工作站上实现了CDAT程序。为了适应手写体的多样性，作者在工作站上利用X-Window系统和C语言编程，分别从109个草书假名字母中选取5种手写体汉字，制作了学习和测试数据集。然后，用交叉验证法将CDAT与典型判别分析进行比较。因此，在书写者之间存在多种笔迹的情况下，CDAT优于典型判别分析，其中需要阈值，使得由决策树划分的子区域不会变得太小。在数据集的非线性结构中，CDAT算法对决策树的分层递归是有效的。

As non-parametric methods of classifying based on decision tree, CART and C4. 5 are typical. In this research, an import of such a non-parametric approach based on decision tree into canonical discriminant analysis is discussed. We call the decision tree based approach which has been imported into canonical discriminant analysis CDAT (Canonical Discriminant Analysis Tree). In CDAT, the index of improvement by dividing a region into two is defined as the difference between before-division sum of eigenvalues for between-groups variance-covariance matrix against within-groups variance-covariance matrix and after-division one. By using programming language C on work station, a routine of CDAT was implemented. For the sake of variety of handwritings, 3 writers prepared learning and testing data sets which consisted of 5 kinds of handwritten characters selected out of 109 cursive kana letters respectively, by using X-window system and programming language C on work staton. Then, CDAT was compared with canonical discriminant analysis with the above data sets, by using cross-validation. As a result, in the case that there was a variety of handwritings between writers, CDAT surpassed canonical discriminant analysis, where a threshold was needed so that subregion divided by decision tree might not become too small. In nonlinear structure of data sets, CDAT is effective to pile strata of decision trees recursively.