Lattice-cell：混合文本分类方法
Lattice-cell : Hybrid approach for text categorization
摘要：
本文提出了一种新的基于概念格和元胞自动机的文本分类框架。在这个框架中，概念结构是由一个元胞自动机符号归纳（CASI）。我们的目标是减少由概念格引起的时间分类。我们研究，通过实验所提出的方法的性能，并将其与其他算法，如朴素贝叶斯和k近邻。结果表明，性能的改善，同时减少分类的时间。
英文摘要：
In this paper, we propose a new text categorization framework based on Concepts Lattice and cellular automata. In this framework, concept structure are modeled by a Cellular Automaton for Symbolic Induction (CASI). Our objective is to reduce time categorization caused by the Concept Lattice. We examine, by experiments the performance of the proposed approach and compare it with other algorithms such as Naive Bayes and k nearest neighbors. The results show performance improvement while reducing time categorization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
