1)KNNK近邻分类
1.PSO Based Feature Weighting Algorithm for KNN;基于PSO面向K近邻分类的特征权重学习算法
英文短句/例句
1.Feature Selection Based on Margin of K-Nearest Neighbors基于K近邻分类间隔的特征选择方法研究
2.Classification of Brain-Computer Interface Signals Based on Common Spatial Patterns and K-Nearest Neighbors基于共空间模式和K近邻分类器的脑-机接口信号分类方法
3.K-NN, K-means and the Application in Text Mining;K-近邻、K-均值及其在文本分类中的应用
4.Web Image Mining of K-NN Classification Algorithm Using Relevance Feedback;相关反馈K-邻近分类Web图像数据挖掘
5.Study on KNN arithmetic based on cluster基于簇的K最近邻(KNN)分类算法研究
6.Network Text Classification Based on K-Nearnest neighbor Method基于K-近邻方法的网络信息文本分类
7.An application of K-nearest neighbor algorithm for the teaching of web page classificationK近邻算法在教学网页分类中的应用
8.Iris Recognition Method and Its Implementation Based on k-NN Classification Matching;基于k近邻分类匹配的虹膜识别方法及实现
9.Study on Feature Transformation Algorithm Based on k-Nearest-Neighbor Classification Rule;基于k近邻分类准则的特征变换算法研究
10.Development and improvement of K-Nearest Neighbor clustering techniqueK-最近邻分类技术的新发展与技术改进
11.An Ameliorated SYM Classifying Algorithm Combined with kNN一种改进的结合K近邻法的SVM分类算法
12.A Kernel KNN Classifier Based on Gene Expression Programming基于基因表达式编程的核k近邻分类算法
13.A Particle Swarm Optimization Based Rapid K-Nearest Neighbor Classification Algorithm基于微粒群优化的快速K-近邻分类算法
14.An Improved Adaptive K Near Neighbor Clustering Algorithm一种改进的自适应K近邻聚类算法
15.Pre-selection Sample Method of Fuzzy KNN in Classify of Support Vector Machines基于模糊k近邻的样本预选取的支持向量机分类算法
16.Algorithm and Simulation of SVM Classifier Based on KNN Judgment基于K最近邻决策的支持向量机分类算法及仿真
17.Incremental Hypertext Classification Based on Fuzzy K-Nearest Neighbor and Evidence Theory基于模糊K最近邻和证据理论的增量式超文本分类方法
18.Multi-feature fusion method based on support vector machine and k-nearest neighbor classifier基于支持向量机和k-近邻分类器的多特征融合方法
相关短句/例句
K-Nearest Neighbour Algorithm(K-NN)K-邻近分类
3)k-nearest neighbour classificationk-近邻分类
1.The k-nearest neighbour classification is a very popular and successful nonparametric classification method, but its classification performance usually suffers from the existing outliers.k-近邻分类是一种流行且成功的非参数分类方法,但其分类性能由于离群点的存在而受到损害。
4)Knearest neighbor algorithmK-近邻分类法
5)kernel-based K-nearest neighbor classification核K近邻分类
6)K Nearest Neighbor Classifierk近邻分类器
延伸阅读
近邻法分类 对被识别样本某个给定近邻域中的已知类别的学习样本数量进行统计，并以其中数量最多的那一类作为分类结果的分类方法。对 k个被识别样本的近邻学习样本进行计算时,假设离被识别样本最近的5个学习样本中有3个属于某类,就把被识别样本判别为该类。当k等于1时，就是通常所说的最近邻规则，即被识别样本离哪一类的学习样本最近，就把它分到哪一类（见最小距离分类）。设R1，R2...，R0分别是已知类别的c个学习样本集合，每个集合Rj中有uj个特征向量，用x忋表示，k=1，2,...,uj。在用最近邻规则时,可以定义被识别特征向量y与Rj之间的距离为 式中‖·‖是给定的一种距离度量。分类器把被识别模式分类到d(y, Rj)值最小的那一类中去。当用欧氏距离作为距离度量时，可以证明这种方法实质上是一种分段线性分类器。理论分析表明,当学习样本无限增加时,用最近邻规则分类的结果，其误识率（错分率）不会超过贝叶斯分类器误识率的两倍。
