منابع مشابه
A Modified Editing k-nearest Neighbor Rule
Classification of objects is an important area in a variety of fields and applications. Many different methods are available to make a decision in those cases. The knearest neighbor rule (k-NN) is a well-known nonparametric decision procedure. Classification rules based on the k-NN have already been proposed and applied in diverse substantive areas. The editing k-NN proposed by Wilson would be ...
متن کاملA Novel Weighted Voting for K-Nearest Neighbor Rule
K-nearest neighbor rule (KNN) is the wellknown non-parametric technique in the statistical pattern classification, owing to its simplicity, intuitiveness and effectiveness. In this paper, we firstly review the related works in brief and detailedly analyze the sensitivity issue on the choice of the neighborhood size k, existed in the KNN rule. Motivated by the problem, a novel dual weighted voti...
متن کاملA Generalization of the k-Nearest Neighbor Rule
A fami ly of superv ised, nonparametric dec is ion r u l e s , based on to lerance reg ions , is descr ibed which inc ludes the k-Nearest Neighbor dec is ion r u l es when there are two c lasses . There are two p r a c t i c a l reasons f o r doing so: f i r s t , a fami ly of dec is ion ru les s i m i l a r to the k-Nearest Neighbor ru les can be spec i f i ed which app l ies to a broader c o ...
متن کاملNearest-Neighbor Classification Rule
In this slecture, basic principles of implementing nearest neighbor rule will be covered. The error related to the nearest neighbor rule will be discussed in detail including convergence, error rate, and error bound. Since the nearest neighbor rule relies on metric function between patterns, the properties of metrics will be studied in detail. Example of different metrics will be introduced wit...
متن کاملConvergence Rate of the Fuzzy Generalized Nearest Neighbor Rule
tizzy k nearest neighbor rule (k-NNR) has been applied in a variety of substantive areas. Yang and Chen [l] described a fuzzy generalized k-NN algorithm which is a unified approach to a variety of fuzzy k-NNR’s. They created the strong consistency of posterior risk of the fuzzy generalized NNR. In this paper, we give their convergence rate. That is, the convergence rate of posterior risk of the...
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ژورنال
عنوان ژورنال: Information and Control
سال: 1970
ISSN: 0019-9958
DOI: 10.1016/s0019-9958(70)90081-1