在Lucene 2.3中对拥有存储字段和Term向量的文档进行了大量的优化，以节省大索引合并的时间。

In Lucene 2.3 there are substantial optimizations for Documents that use stored fields and term vectors, to save merging of these very large index files.

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利用假币辨识向量集，引入搜索矩阵，给出一种逐列相加合并的算法，解决了单假币辨识的完全非适应算法问题。

Utilizing certain sets ofm-dimensional vectors and introducing search Matrix, this paper designs an arithmetic that adds and merges row by row at search Matrices.

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为此，改进了传统KNN算法，将训练文本中相似度大的文本合并，称为一簇，并计算簇的中心向量。

So, the traditional KNN arithmetic, clusters training document with highly overlapping word is improved, central vector of cluster is gained.

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