本文在分析了两类方法的优缺点后，提出了将特征驱动的方法(K - D森林)引入词汇树模型的混合模型。

The thesis analyzes the advantages and drawbacks of the two kinds of algorithms and proposes a mixture model which introduces the feature driven methods (K-D Forest) into vocabulary tree.

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首先提出了网络门限变量的两个化简原则及计算网络K-树和极小K-割的算法。

Two gate variable reduction principles, algorithms of computing K-trees and minimum K-cuts of a network are proposed firstly.

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着重证明了K -树组法为多项式时间复杂性算法。

It is proved that K-Tree Term method is a multinomial time complexity algorithm.

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