Description: 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_SearchNN_2.dll - 近邻点搜索缓存2
参考文献：
M.B.Kennel, R.Brown, H.D.I.Abarbanel. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Phys. Rev. A 1992,45:3403.
-false neighbors (False Nearest Neighbor, FNN) calculation embedding dimension of the Matlab program folder : Main_FNN.m-procedure main function, Direct operating this document can be LorenzData.dll-time series produced Lorenz PhaS paRecon.m-phase space reconstruction fnn_luzhenbo.dll-calculated at the main function neighbors SearchNN.dll-point search buffer_SearchNN_1.dll neighbor- Search neighbor point a buffer_SearchNN_2.dll Cache-Cache Search neighbors point two reference Literature : M. B. Kennel, R. Brown, H. D. I. Abarbanel. Determining embedding dime nsion for phase-space reconstruction using a g eometrical construction [J]. Phys. Rev. A 1992 , 45:3403. Platform: |
Size: 99328 |
Author:呆雁 |
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Description: 用matlab实现的kdtree，kdtree适合用于数据挖掘中用于寻找最近邻。-Using matlab to achieve kdtree, kdtree suitable for data mining used to find the nearest neighbors. Platform: |
Size: 102400 |
Author:项龙江 |
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Description: 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 work on double, it is possible to choose if return the distances.
Here is a time comparison with a vectrized m-code:
N=1000000 number of reference points
Nq=100 number of query points
dim=3 dimension of points
k=3 number of neighbor
tic
[idc,dist]=BruteSearchMex(p ,qp , k ,k) MEX
toc
tic
[idc,dist]=knnsearch(qp,p,k) VECTORIZED M-CODE
toc
p=rand(N,dim)
qp=rand(Nq,dim)
Output:
Elapsed time is 0.962640 seconds.
Elapsed time is 18.813100 seconds. Platform: |
Size: 4096 |
Author:Ming |
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Description: 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. Platform: |
Size: 4096 |
Author:刘斌 |
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Description: FAST K-NEAREST NEIGHBORS SEARCH
-FAST K-NEAREST NEIGHBORS SEARCH
Description You can find the description at:
http://www.advancedmcode.org/gltree.html
A Pro Version has been published on:
http://www.advancedmcode.org/gltree-pro-version-a-fast-nearest-neighbour-library-for-matlab-and-c.html
Acknowledgements The author wishes to acknowledge the following in the creation of this submission:
K-NEAREST NEIGHBOURS AND RADIUS (RANGE) SEARCH
This submission has inspired the following:
K-NEAREST NEIGHBOURS AND RADIUS (RANGE) SEARCH, FAST K-NEAREST NEIGHBOURS SEARCH 3D VERSION
MATLAB release MATLAB 7.5 (R2007b)
Other requirements Need a mex compiler
Zip File Content
Other Files BuildGLTree2DFEX.cpp,
BuildGLTree2DFEX.m,
DeleteGLTree2DFEX.cpp,
DeleteGLTree2DFEX.m,
GLTree2DFEX.cpp,
GLTree2DFEX.h,
license.txt,
NNSearch2DFEX.cpp,
NNSearch2DFEX.m,
TestMexFiles.m
Platform: |
Size: 8192 |
Author:谢冉 |
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Description: 分类工具包，含有多个分类算法的matlab源程序，适用于类别数据的分类和可视化-The Classification toolbox for MATLAB is a collection of MATLAB modules for calculating classification (supervised pattern recognition) multivariate models: Discriminant Analysis, Partial Least Square Discriminant Analysis (PLSDA), Classification trees (CART), K-Nearest Neighbors (kNN) and Soft Independent Modeling of Class Analogy (SIMCA).
This is the version 3.1 of the Classification toolbox for MATLAB
Classification toolbox for MATLAB has been released by Milano Chemometrics and QSAR research Group.
Visit our website at www.disat.unimib.it/chm
Platform: |
Size: 505856 |
Author:Alex |
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Description: 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 neighbor (KNN) algorithm. In detail, for each unseen
instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of
these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle
is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast
gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance
to some well-established multi-label learning algorithms.
2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.) Platform: |
Size: 5120 |
Author:玖
|
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Description: 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 you are interested in.) Platform: |
Size: 2048 |
Author:歌尽桃花月 |
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Search - k-nearest neighbors matlab