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k近邻算法 均值算法 中值算法 针对高斯和椒盐图像去噪-means algorithm k nearest neighbor algorithm in the value of denoising for Gaussian and salt and pepper
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K最近邻(K-Nearest Neighbor，KNN)分类算法，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。-K nearest neighbor (K-Nearest Neighbor, KNN) classification algorithm, is a more mature approach in theory is the simplest machine learning algorithms.
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knn分类算法 遥感图象分类
knn分类算法 遥感图象分类 -knn classify
k-nearest neighbor algorithm
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
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k-nearest-neighbor K近邻算法的C++实现，训练后模型可以对测试数据进行归类。找了一些PUDN上的程序发现都没有很好的训练和测试数据集，所以自己写了一个直接放在main函数里面，不要从外部文件读取。工程运行于vc++ 6.0环境。Debug目下exe文件可以直接双击运行查看结果。-K nearest neighbor of C++ implementation, training model can be classified on the test data. Find so
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KNN分类器（K-Nearest Neighbor classification）可以简单得将几种类别分开 -KNN classifier
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k-Nearest Neighbor to Matlab.
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K nearest neighbor algorithm
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一个用K近邻实现分类的算法，k最近邻算法是模式识别中的一种比较简单而经典的分类算法-Achieved with a K-nearest neighbor classification algorithms, k-nearest neighbor algorithm for pattern recognition is a relatively simple and classic classification algorithm
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knn（k-近邻）用于模式识别，实验所需数据已给出，适用于初学者加深对knn算法的理解。-The knn（(k-Nearest Neighbor）classification algorithm is used for pattern recognition, the experimental data required has been given to deepen the understanding of the knn algorithm for beginners .
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人工智能里面的几个c程序，蚁群算法、k近邻、hebb学习、hopfield网络、后向传播网络等。-Artificial intelligence inside the c program, ant colony algorithm and k nearest neighbor, hebb study, the Hopfield network, back propagation network.
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用c语言编的几个人工智能程序，包括蚁群算法，k最近邻, hebb学习, Hopfield 网络, 后向传播网络。-Artificial intelligence inside the c program, ant colony algorithm and k nearest neighbor, hebb study, the Hopfield network, back propagation network.
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本文阐述了一个基于K-Nearest Neighbor(KNN)算法的中文网页自动分类系统的体系结构、具体设计和实现过程。系统分成三个模块：中文网页的下载与过滤（王勋完成）；KNN算法的训练与分类（黄健完成）；算法评估以及分词系统的整合（吴亮完成）。
本人主要是实现算法的评估以及分词系统的整合：分词系统的整合利用爱博汉语分词系统(服务器版本—共享版)，将下载下来的中文网页过滤后所得到的txt文件内容进行分词，以便提供给KNN算法进行训练和分类；算法的评估是将KNN算法后所得到的结果进行分析，
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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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提出了一种改进的散乱数据点k近邻搜索算法-An improved algorithm for searching points’k nearest neighbor is presented
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模式识别 K近邻法实现，用c实现-K-nearest neighbor method, Pattern Recognition implementation
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关于metric learning的综述，涉及到许多的知识：SVM、kernel、SDP等-This paper surveys the field of distance
metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different
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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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MLRE算法，选取最优K近邻，是识别率达到更高-MLRE algorithm select the best K-nearest neighbor, is to achieve a higher recognition rate
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使用K近邻法设计分类器，对一组数据进行分类-K-nearest neighbor algorithm to design the classifier, to classify a set of data
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查找二维和三维空间中K最近邻点的算法，在Approximate Nearest Neighbor Searching（ANN）的基础上进行了包装，易于使用。-Find the two-dimensional and three-dimensional space of K nearest neighbor algorithm, Approximate Nearest Neighbor Searching (ANN) based on the packaging.
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