Nearest Neighbor Classifiers: Reducing the Computational Demands

机译：最近的邻邻分类器：降低计算需求

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Nearest Neighbor Classifiers demand high computational resources i.e, time and memory. Two distinct methods are followed by researchers in Pattern Recognition to reduce this computational burden. The first method is reducing the reference set or training set and the second method is dimensionality reduction which are referred as Prototype Selection and Feature Reduction(a.k.a Feature Extraction or Feature selection) respectively. In this paper, we cascaded the two methods to reduce the data set in both directions there by reducing the computational burden of Nearest Neighbor Classifier. The experiments are done on the bench mark datasets and the results obtained are satisfactory.

机译：最近的邻邻分类器需要高计算资源i.e，时间和内存。在模式识别中的研究人员遵循两个不同的方法，以减少这种计算负担。第一种方法正在减少参考集或训练集，第二种方法是分别称为原型选择和特征减少（A.k.a特征提取或特征选择）的维度降低。在本文中，我们通过降低最近邻分类的计算负担来级联两种方法来减少两个方向上的数据集。实验在替补标记数据集上完成，得到的结果令人满意。

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《IEEE International Conference on Advanced Computing》|2016年|1 v.|共6页
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作者
R. Raj Kumar; P. Viswanath; C. Shobha Bindu;
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正文语种
中图分类计算技术、计算机技术;
关键词
Training; Prototypes; Feature extraction; Classification algorithms; Silicon; Conferences; Computer science;

机译：训练;原型;特征提取;分类算法;硅;会议;计算机科学;

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1. An Approach to Reduce the Computational Burden of Nearest Neighbor Classifier [J] . R. Raja Kumar, P. Viswanath, C. Shobha Bindu Procedia Computer Science . 2016,第1期

机译：一种减少最近邻分类器计算负担的方法
2. The Nearest Subclass Classifier: A Compromise between the Nearest Mean and Nearest Neighbor Classifier [J] . Veenman C.J., Reinders M.J.T. IEEE Transactions on Pattern Analysis and Machine Intelligence . 2005,第9期

机译：最近的子类分类器：最近均值和最近邻居分类器之间的折衷
3. Highly flexible nearest-neighbor-search associative memory with integrated k nearest neighbor classifier, configurable parallelism and dual-storage space [J] . An Fengwei, Mihara Keisuke, Yamasaki Shogo, Japanese journal of applied physics . 2016,第4s期

机译：具有集成的k最近邻分类器，可配置的并行性和双存储空间的高度灵活的最近邻搜索关联存储器
4. Nearest Neighbor Classifiers: Reducing the Computational Demands [C] . R. Raj Kumar, P. Viswanath, C. Shobha Bindu IEEE International Conference on Advanced Computing . 2016

机译：最近的邻居分类器：减少计算需求
5. The Effect of K-Nearest Neighbors Classifier for Intrusion Detection of Streaming Net-Flows in Apache Spark Environment [D] . Thevar, Muthukumar 2017

机译：K近邻分类器对Apache Spark环境中流式网络流的入侵检测的影响
6. Prediction of acid radical ion binding residues by K-nearest neighbors classifier [O] . Liu Liu, Xiuzhen Hu, Zhenxing Feng, 2019

机译：用K近邻分类器预测酸性离子结合残基。
7. An Approach to Reduce the Computational Burden of Nearest Neighbor Classifier [O] . Kumar R. Raja, Viswanath P., Bindu C. Shobha 2016

机译：一种减少最近邻分类器计算负担的方法

Nearest Neighbor Classifiers: Reducing the Computational Demands

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