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对朴素贝叶斯算法的进一步改进。朴素贝叶斯分类器是一种简单而高效的分类器,但是它的属性独立性假设使其无法表示现实世界属性之间的依赖关系,以及它的被动学习策略,影响了它的分类性能。本文从不同的角度出发,讨论并分析了三种改进朴素贝叶斯分类性能的方法。为进一步的研究打下坚实的基础-Naive Bayes algorithm further improved. Naive Bayes classifier is a simple and efficient classifier, but its attribute independence assumption it can not be said that the dependencies between the properties of the real world, as well as the passive learning strategies, affect its classification performance. Starting from different points of view to discuss and analyze the three improved the performance of the naive Bayesian classifier. To lay a solid foundation for further study
Date : 2026-01-03 Size : 156kb User : xujingxue
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