Description: k最邻近算法，经典的分类算法，绝对有帮助-k-nearest neighbour algorithm,it is a classical algorithm for text cluster Platform: |
Size: 17408 |
Author:freesunshine |
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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: k-近邻算法 m文件
及其excel示范-this package invulved a m-file example of k-nearest neighbour and its corresponding excel file Platform: |
Size: 38912 |
Author:guoguozhong |
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Description: Compute nearest neighbours (by Euclidean distance) to a set of points of interest from a set of candidate points.
The points of interest can be specified as either a matrix of points (as columns) or indices into the matrix of candidate points.
Points can be of any (within reason) dimension.
nearestneighbour can be used to search for k nearest neighbours, or neighbours within some distance (or both)
If only 1 neighbour is required for each point of interest, nearestneighbour tests to see whether it would be faster to construct the Delaunay Triangulation (delaunayn) and use dsearchn to lookup the neighbours, and if so, automatically computes the neighbours this way. This means the fastest neighbour lookup method is always used. Platform: |
Size: 30720 |
Author:nadir |
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Description: This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.-This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification. Platform: |
Size: 653312 |
Author:kvga |
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Description: The authors present an automatic classification of different power quality (PQ) disturbances using
wavelet packet transform (WPT) and fuzzy k-nearest neighbour (FkNN) based classifier. The training data
samples are generated using parametric models of the PQ disturbances. The features are extracted using
some of the statistical measures on the WPT coefficients of the disturbance signal when decomposed upto
the fourth level. These features are given to the fuzzy k-NN for effective classification. Platform: |
Size: 580608 |
Author:gk |
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Description: 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 test sample, thereby reducing the amount of calculation and improve the speed of classification Platform: |
Size: 4096 |
Author:houying |
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Search - k nearest neighbour