一种搜索编码法及其在监督分类中的应用

纠错输出码作为监督分类领域中的一个新的研究方向,是提高分类器泛化能力的一种有效方法,但目前还没有通用的确定性编码方法.分析了现有纠错输出码的性质,提出一种搜索编码法,该方法通过对整数空间的顺序搜索,获得满足任意类别数目与最小汉明距离要求的输出码;然后探讨了基于搜索编码的监督分类技术.对简单贝叶斯与BP神经网络算法进行实验,结果表明,搜索编码法可作为一种通用的编码方法用于提高监督分类器的泛化能力.

A Search Coding Method and Its Application in Supervised Classification

Supervised classification based on error-correcting output codes (ECOC) is a new research direction to improve the generalization of classifiers, yet there is no general method to construct ECOC for any number of classes. This paper analyzes the properties of ECOC and presents a search coding method which corresponds to codewords with integers and gets a satisfied output code through searching an integer range in sequence. It then describes the supervised classification technique based on the search coding method. By applying the search coding method to na?ve-Bayes algorithm and BP neural networks, experimental results show that the method is an effective and general coding method to construct error-correcting output codes.