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k-nearest neighbors classifier
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KNN算法的实现，k-nearest neighbors聚类算法的matlab 实现
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knn—k近邻准则matlab实现，适合初学者,knn-k neighbors to achieve the criteria matlab
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常用的分类方法，包括最近邻（NN），k均值（kmeans），k近邻，Fisher线性判别。-Commonly used classification methods, including nearest-neighbor (NN), k the mean (kmeans), k neighbors, Fisher linear discriminant.
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数据挖掘导论中的K近邻聚类算法，用C++编写而成。-Introduction to Data Mining of the K neighbors clustering algorithm, using C++ has been prepared by.
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K-nearest neighbors 搜索 聚类时经常使用的一种方法 国外网站转载- The following utilities are provided:
- Nearest neighbor
- K-Nearest neighbors
- Radius Search
They al supports N-dimensions and wor
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FAST K-NEAREST NEIGHBORS SEARCH
-FAST K-NEAREST NEIGHBORS SEARCH
Descr iption You can find the descr iption at:
http://www.advancedmcode.org/gltree.html
A Pro Version has been published on:
http://www.advancedmcode.org/gltree-pr
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classification algorithm. We use the genetic algorithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algorithm to classify new data records based on their we
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近邻法的基本思想是在测试样本x的k个近邻中，按出现的样本类别来作为x的类别，即先对x的k个近邻一找出它们的类别，然后对x类别进行判别，即在N个训练样本中，找出x的k个近邻。-The basic idea is that neighbors in a test sample x, k-nearest neighbor in categories according to the sample appears as a type of x, ie the first k nearest neighb
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program to find the k - nearest neighbors (kNN) within a set of points.
Distance metric used: Euclidean distance
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K-Nearest Neighbors Algorithm
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LTSA[19] is based on the intuition that when a manifold is correctly unfolded, all of the tangent hyperplanes to the manifold will become aligned. It begins by computing the k-nearest neighbors of every point. It computes the tangent space at every p
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In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approx
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FAST K-NEAREST NEIGHBORS SEARCH，
Simple but very fast algorithm for nearest neighbors search in 2D space.
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近邻法的基本思想是在测试样本x的k个近邻中，按出现的样本类别来作为x的类别，即先对x的k个近邻一一找出它们的类别，然后对x类别进行判别，即在N个训练样本中，找出x的k个近邻。-The basic idea of the nearest neighbor method in the test sample x k nearest neighbors, according to the type of the sample of x as a category, that is, first k n
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采用快速K近邻与Kmeans聚类算法来计算前K个近邻，舍弃了一部分不可能成为待测样本的前K个近邻的训练样本，从而减少了计算量，提高了分类速度-Fast K-nearest neighbor Kmeans clustering algorithm to calculate the K nearest neighbors, abandoning the training samples of the part can not become the first K neighbors of the t
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K MEAN CLUSTERING FOR IMAGE PROCESSING
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k-nearest-neighbors classification with incremental update of distance matrix
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k最近邻法、有权重的k最近邻法及线性判别-K-nearest neighbor and linear discriminant analysis
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适用于对模式识别感兴趣的同学，是K近邻的MATLAB源码，希望对你有帮助，这个代码可以直接运行，你可以更改自己感兴趣的参数。(For students who are interested in pattern recognition, they are MATLAB codes for K nearest neighbors. I hope they can help you. This code can run directly, you can change the parameters
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