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- 一本将基于近邻传播算法的半监督聚类的算方法书.对于聚类研究的很有帮助-Abstract: A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this paper. AP takes as input measures of similarity between pairs of data points. AP is an efficient a
knn
- java语言实现的KNN算法代码。 KNN就是K最近邻(k-Nearest Neighbor,KNN)分类算法-java language code of the KNN algorithm. KNN is a K-nearest neighbor (k-Nearest Neighbor, KNN) classification algorithm
flann-1.6.11-src
- 快速计算近似最近邻域,可用于图形处理中计算点云的k-Quickly compute approximate nearest neighbor, can be used for graphics processing point cloud computing knn
kdtree
- kd 近邻查找. 一般用在图像拼接等领域-k-d tree to find the nearest one
ren-gong-zhi-neng
- 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.
philbinj-fastann-cbf02be
- 近似最近邻多维向量快速匹配算法,使用随机k-d树-FASTANN: A library for fast approximate nearest neighbours
extendimage
- 根据模板大小对图像扩展,最近邻法,matlab-According to the template image size on expansion, k-nearest-neighbors, matlab
ANNTest
- 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.
KNN
- K最近邻C实现,测试通过,可以使用,主要适用于数据库定位方法-K nearest neighbor C implementation, the test passes, you can use, mainly applied to the database location method
Semi-supervised-learning
- 义了一个欧氏距离和监督信息相混合的新的最近邻计算函数,从而将K一均值算法很好地应用于半 监督聚类问题。针对K一均值算法初始质心敏感的缺陷,用粒子群算法的搜索空间模拟聚类的欧氏空间,迭代搜 索找到较优的聚类质心,同时提出动态管理种群的策略以提高粒子群算法搜索效率。算法在UCI的多个数据集 上测试都得到了较好的聚类准确率。-Righteousness of a Euclidean distance and supervision of a mixture of new nearest n
location
- 室内定位算法,最近邻算法,K最近邻算法,加权K最近邻算法,贝叶斯算法-indoor position locatian ,including nn,knn,wknn,bayes.the algrithm compare the different of the above method and get some useful conclusion.
20257147knn
- knn最近邻算法在给定新文本后,考虑在训练文本集中与该新文本距离最近(最相似)的 K 篇文本,根据这 K 篇文本所属的类别判定新文本所属的类别,具体的算法步骤如下: 一、:根据特征项集合重新描述训练文本向量 二、:在新文本到达后,根据特征词分词新文本,确定新文本的向量表示 三、:在训练文本集中选出与新文本最相似的 K 个文本-knn nearest neighbor algorithm in the given text, to consider in the train
KKNNn
- Knn算法综述、柔性KNN算法研究、一个高效的knn分类算法法、一种改进的KNN分类算法、一种优化的K最近邻协同过滤算法。 -The Knn algorithm summarized flexible KNN algorithm, an efficient knn classification algorithm method, an improved KNN classification algorithm, an optimized K nearest neighbor collabor
knn
- 本程序中,训练样本集含有30个样本,矢量长度为5,对样本{1,18,11,11,0.5513196}进行K=5的K-最近邻分类. 样本从文件data.txt中读取,程序运行结果显示所有样本以及其类别,待分类样本所属的类别({1,18,11,11,0.5513196}属于"2"类),以及它的5个最近邻的类别和与它之间的距离。-In this program, the training sample set containing 30 samples, the vector length
DM_YeDan
- KNN(K最近邻)分类算法以及K-means(K均值)聚类算法是应用广泛的两种算法。本代码是在VS2010环境下,用 C++语言在基于KNN及K-means算法下,实现了对Iris数据集的分类与聚类。-KNN (K nearest neighbor) classification algorithm, as well as K-means (K mean) clustering algorithm is widely used two algorithms. The code VS2010 en
KDTree
- k-d树(k-dimensional树的简称),是一种分割k维数据空间的数据结构。主要应用于多维空间关键数据的搜索(如:范围搜索和最近邻搜索)。用java实现,可直接运行-kd-tree (k-dimensional tree for short), is a partition the data structure of k-dimensional data space. Mainly used in the search of the key data of the multidimensi
KDTree2Kmeans
- 基于kdtree的kmeans聚类算法,在选择最近邻中心点时使用kdtree的检索功能,提高了检索效率,特别是当k较大时,效果明显提升-kmeans clustering algorithm based on kdtree,used in selecting the center point of the nearest neighbor the kdtree retrieval function, and improve the retrieval efficiency has improve
littleworld
- NW小世界网络的构成原则为:从一个环状的规则网络开始,网络含有N个结点,每个结点向与它最近邻的K个结点连出K条边,并满足N>>K>>In(N)>>1。随后进行随机化加边,以概率p在随机选取的一对节点之间加上一条边。其中,任意两个不同的节点之间至多只能有一条边,并且每一个节点都不能有边与自身相连。改变p值可以实现从最近邻耦合网络(p=0)向全局耦合网络(p=1)转变。在p足够小和N足够大时,NW小世界模型本质上等同于WS小世界模型。 -NW constitu
1
- 利用K-L变换进行人脸识别。首先求得待辨识图像相对于训练集平均脸的差值图像,然后求得该图像在特征脸空间中的坐标,最后采用最近邻法对图像进行归类。-KL transform for face recognition. Obtain the first image to be identified image with respect to the difference between the average face of the training set, and then obtain the
pattern1_a
- . PCA人脸识别 A.闭集测试。用每个人的前5张图像作为训练,剩下的5张图像作为测试。也就是说总共有200张训练图像和200张测试图像。采用最近邻分类,分析选取不同的主分量个数K,对识别率的影响 -. PCA Face Recognition A. Closed set tests. With each of the first five images for training, the remaining 5 images as a test. That is a total of