基于数据块混合度量的加速K-近邻分类方法OACSTPCD
Speeding K-NN Classification Method Based on Data Block Mixed Measurement
针对标准K-近邻分类方法( K-Nearest Neighbor, KNN)在新样本类别预测过程中需要计算新样本与所有已标记样本距离而导致分类效率低,不能有效处理大规模数据分类的问题,本文提出一种基于数据块混合度量的加速K-近邻分类( KNN Method Based on Data Block Mixed Measurement, KNN_DBM2)方法。该方法将数据块的混合度量引入K-NN的预测类别过程,首先将已标记的数据划分为不同的数据块,…查看全部>>
This paper presents a K-Nearest Neighbor ( KNN) method based on data block mixed measurement, called KNN_DBM2 , in order to solve the problem that the low training efficiency and cannot solve the large scale problems of normal K-NN be-cause it needs compute the distance between the sample to be tested and the labeled samples in the new sample classification pre-diction process. By introducing the data block mixed measurement into the prediction process of K-…查看全部>>
邓曦辉;赵丽
晋中学院信息技术与工程学院，山西 晋中 030619晋中学院信息技术与工程学院，山西 晋中 030619
信息技术与安全科学
K-近邻数据块混合度量预测性能KNN_DBM2算法
K-nearest neighbordata blockmixed measurementprediction efficiencyKNN_DBM2 algorithm
《计算机与现代化》 2016 (12)
47-50,4
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