该文根据自相关函数与谱密度函数之间的对应关系，提出了一种新的基于自相关函数的决策树归纳学习算法。
According to the relationship between auto correlation function and its spectral density, a new type of decision tree method based on signal analysis theory is proposed in this paper.
论文重点研究了决策树内部结点的特征选择方法，设计了一个新的距离差函数。
The research emphasizes on the feature selection in internal-nodes, and designs a new Distance Difference Function.
提出了以否定树为基础的解决SOP型函数求补运算的新算法。
A new algorithm is given to find complements of functions in SOP form based on Algorithm in this paper.
应用变精度粗糙集理论，提出了一种利用新的启发式函数构造决策树的方法。
A new heuristic function to build decision trees based on variable precision rough set is proposed.
应用变精度粗糙集理论，提出了一种利用新的启发式函数构造决策树的方法。
A new heuristic function to build decision trees based on variable precision rough set is proposed.
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