搜索资源列表
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假近邻法(False Nearest Neighbor, FNN)计算嵌入维的Matlab程序
文件夹说明:
Main_FNN.m - 程序主函数,直接运行此文件即可
LorenzData.dll - 产生Lorenz时间序列
PhaSpaRecon.m - 相空间重构
fnn_luzhenbo.dll - 假近邻计算主函数
SearchNN.dll - 近邻点搜索
buffer_SearchNN_1.dll - 近邻点搜索缓存1
buffer_Search
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用得最多的是最近邻,此处上传的是K-近邻,即k=1。matlab环境下的代码。附有实例。-used most often is the nearest neighbor, here is uploaded K-neighbor, k = 1. Matlab environment code. With examples.
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脱机手写体识别Matlab源程序
包括特征提取、bayes分类器、K近邻分类及最近邻分类。
Testscr iptRecognition.m:测试代码
scr iptFeaExtract.m :特征提取
KNearestEstimate.m :K近邻估计
NearestEstimate.m : 最近邻估计
BayesTrain.m :训练bayes分类器
Bayes.m :测试bayes分类器
CrossValidate.m :m交叉验证
-Offlin
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K近邻算法(KNN)的matlab源代码,程序清晰易读-K nearest neighbor (KNN) of matlab source code, procedures legible
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本文设计基于DCT的人脸识别系统,首先结合当今人脸识别的背景和发展状况讨论了人脸识别的研究内容及在各方面的应用;然后研究了人脸识别进行预处理,讨论了人脸识别预处理的其他方法,分析各种方法的利弊,最后采用DCT(离散余弦变换)实现人脸图像预处理中的降维处理;接下来对人脸图像的特征提取进行了研究,简单叙述了几何特征提取和代数特征提取,同时深入研究了基于DCT和PCA变换的人脸图像特征提取,从而实现是否对人脸识别系统识别率有所提高的研究;对于分类器的选择,本文对两种分类器进行了探讨,即最近邻分类器和B
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k近邻算法(knn, k nearest neighbor)-k nearest neighbor (knn, k nearest neighbor)
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此文件包含模式识别中一些经典的matlab代码,如:SVM、神经网络、最近邻等。-This file contains some of the classic pattern recognition matlab code, such as: SVM, neural networks, nearest neighbor and so on.
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使用matlab实现的KNN分类器,可以设置最近邻的个数-Implemented using the KNN classifier matlab, you can set the number of nearest neighbor
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k-Nearest Neighbor to Matlab.
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用matlab实现K近邻算法,用于数据挖掘的分类-K-nearest neighbor algorithm for the classification of data mining using matlab
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十大经典人工智能算法之一——K最近邻Matlab实现-One of the top ten classical artificial intelligence algorithms- K nearest neighbor Matlab implementation
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基于PCA降维的KNN,最近邻分类matlab实现。-PCA dimensionality reduction based the KNN, the nearest neighbor classification matlab.
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经典的knn最近邻算法 利用matlab的内部JIT加速算法,简单好用,高速时效。-Classic knn nearest neighbor algorithm using matlab internal JIT accelerated algorithm, easy to use, high-speed aging.
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This toolbox contains C++ files that implement the
Approximate Nearest Neighbor Library (ANN)
and and its utility functions in matlab.
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非线性分类器,包括ANN-BP算法的matlab代码实现和性能分析,以及近邻法的实现-Non-linear classifiers, including ANN-BP algorithm matlab code and performance analysis, and nearest neighbor methods to achieve
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数字识别的matlab的程序,采用了最近邻发来实现-matlab program identification number, using the nearest neighbor sent achieved
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ML-KNN,这是来自传统的K-近邻(KNN)算法。详细地,为每一个看不见的实例中,首先确定了训练集中的k近邻。之后,基于从标签集获得的统计信息。这些相邻的实例,即属于每个可能类的相邻实例的数量,最大后验(MAP)原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究,即酵母基因功能分析、自然场景分类和网页自动分类,表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbo
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Matlab代码,根据算法原理自己编写的基本算法的代码,有:KNN,层次聚类,C均值,最邻近算法。包括自己挑选的数据集,对算法准确率的测试。(Matlab code, the code of the basic algorithm written by itself according to the algorithm principle, there are: KNN, hierarchical clustering, C-means, nearest neighbor algorithm.
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