KNN algorithm is a common and effective text categorization algorithm.
KNN算法是一种常用的效果较好的文本分类算法。
In basic KNN algorithm, the K is fixed for different processing texts, and the weights of similarity for neighbors are equal.
但是标准KNN算法中，近邻的数目K对所有处理文本都是一样的，而判断类别时加权的仅仅是文本之间的相似度。
Improved KNN algorithm by using the combination technology, and applied it to the forecast of non-mining fracture of shaft-lining of mine.
利用组合技术对KNN算法进行改进，并将其应用于煤矿立井井筒非采动破裂的预测。
The experiment result has proved that the method can improve the class's categorization effect with fewer training samples of KNN algorithm.
实验结果表明此方法有效改善了KNN算法对少数类分类效果。
In this paper, a flexible KNN algorithm is developed with varying-K algorithm and weighting algorithm, which improves the effect of text categorization.
基于近邻序列的排序，提出了变k算法，并且结合效果较好权重算法，形成了柔性的KNN算法，提高了分类的效果。
Feature weighting is one of the important problems for feature weighting based KNN algorithm, and many heuristic methods have been employed to solve the problem traditionally.
特征权重学习是基于特征赋权的K近邻算法需要解决的重要问题之一，传统上提出了许多启发式的学习方法。
The kernel based weighted KNN algorithm solves the multi peak distribution problem and the overlap boundary problem of the sample set, as well as the classifier's precise decision problem.
基于核的距离加权KNN算法解决了样本的多峰分布、边界重叠问题和分类器的精确分类决策问题。
It plays an instructional role in academic study and practical application of KNN text classification algorithm.
对KNN文本分类算法的理论研究和实际应用起了指导作用。
The KNN based music source separation algorithm utilizes prior information of music source which can also achieve music source separation.
K最近邻的信号分离算法中，较多地采用了乐音信号的先验信息，一定程度上可实现信号分离。
This algorithm combine advantages of KNN and Clustering, decreasing training samples and quantity of algorithm calculating, and increasing the speed of retrieval.
该算法将聚类方法和KNN算法的优点结合起来，从而达到缩减了训练样本数量，减少了算法计算量，加快检索速度的目的。
This paper presents a fast text classification algorithm based on KNN (K Nearest Neighbor).
提出了一种基于K近邻(KNN)原理的快速文本分类算法。
Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.
基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。
Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.
基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。
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