基于改进的混合学习模型的手写阿拉伯数字识别方法

在特征空间维数较高的手写阿拉伯数字识别问题中，冗余的特征往往会意外增加学习模型刻画问题空间的复杂度，影响手写阿拉伯数字识别的效率和精确度。该文提出了一种基于边界对特征的敏感度值进行特征选择的支持向量机树混合学习模型，依据当前中间节点上的分类曲面对子样本空间中的样例特征的敏感程度选择特征，在新构建的子样本集上训练子节点上的支持向量机。UCI机器学习数据库中手写阿拉伯数字识别问题的仿真结果表明，与其他算法相比，该文提出的方法能够在提高或保持手写阿拉伯数字高识别精确率的同时，精简问题空间，从而简化混合学习模型的中间节点和整体结构。

An Improved Hybrid Learning Model Based Handwritten Digits Recognition Approach

This paper is concerned with the problem of handwritten digits recognition in high dimensional feature space. The residual feature information may bring undesirable complexity to the underlying probability distribution of the concept label for learning algorithm to capture. The recognition accuracy and efficiency of the so trained learning model are usually depressed. According to this situation, an improved confusion-crossed support vector machine tree is proposed. A feature selection process based on the sensitivity of the margin to a feature is presented for the training step of each support vector machine embedded in each internal node. The experimental results on optical handwritten digits recognition problem in UCI database indicate that the proposed approach achieves competitive or even better recognition accuracy in the condensed feature space. Further, it also obtains lower structure complexity on internal nodes and the whole hybrid learning model than the compared approaches.