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 this paper, we proposed an automatic keyword extraction method based on KNN method.
本文利用K最近邻方法的思想，提出了一种基于K最近邻的关键词自动抽取方法。
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。
AIRS results in the memory cell pool after it is trained and classifies the original antigens by KNN.
AIRS通过训练产生记忆细胞池，利用最近邻原理对原始抗原分类。
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。
As we all know, KNN method is a classic method in machine learning field and is also well used in many other fields.
众所周知，K最近邻方法作为机器学习领域的一个经典的方法，在很多领域都有出色的表现。
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。
KNN query is one of the most representative queries in multimedia database management system. It shows the result of k objects nearest to the query point.
KNN查询是多媒体数据库管理系统中最具代表性的查询方式之一，它将k个与查询点最接近的对象作为查询结果返回。
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。
Experimental results show that B-KNN method remarkably outperforms KNN method, and it is more suitable for classification tasks with deep hierarchy categorization space.
实验结果表明，与KNN相比，B - KNN的性能有较大提升，更适用于具有较深层次类型空间的文本分类应用。
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。
The traditional KNN has a fatal defect that time of similarity computing is huge. The practicality will be lost when the KNN is applied to WEB text categorization with high dimension and huge samples.
传统KNN方法的明显缺陷是样本相似度的计算量很大，使其在具有大量高维样本的Web文本分类中缺乏实用性。
The traditional KNN has a fatal defect that time of similarity computing is huge. The practicality will be lost when the KNN is applied to WEB text categorization with high dimension and huge samples.
传统KNN方法的明显缺陷是样本相似度的计算量很大，使其在具有大量高维样本的Web文本分类中缺乏实用性。
应用推荐