Description: SVM是一种常用的模式分类机器学习算法，以效率高准确度高闻名于世，libsvm和svmlight是常用的两种SVM实现方法。-SVM is a commonly used pattern classification machine learning algorithm, to high accuracy and efficient world-famous, libsvm and svmlight is commonly used to achieve the two SVM methods. Platform: |
Size: 753664 |
Author:De-Chuan Zhan |
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Description: BP网络的应用： BP神经网络用于分类与回归， 使用matlab打开-Application of BP Network: BP neural network for classification and regression, the use of matlab to open Platform: |
Size: 3072 |
Author:ycs |
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Description: 由国外著名大学编写的非常有效近似最近邻分类算法的源码与库，可直接使用，也可作为学习-Well-known universities from abroad prepared very effective approximate nearest neighbor classification algorithm source code and libraries, can be used directly, can also be used as study Platform: |
Size: 1166336 |
Author:李绍柱 |
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Description: matlab图像处理工具相，使用了主成分分析，ANN，SVM等方法。-This toolBox used in the image processing(feature extraction and classification)
PCA,LDA,ICA,DCT,RBF,RBE,GRNN,KNN,minimum distance,SVM, and others Platform: |
Size: 74752 |
Author:大长今 |
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Description: 应用MATLAB中神经网络，实现分类与识别-Application of MATLAB in the neural network to realize classification and identification Platform: |
Size: 6144 |
Author:lk |
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Description: VC++实现的神经网络工具库，可用于分类，模式识别，数据拟合插值等，具有图形显示功能-ANN is developed by VC++，for classification, pattern recognition, data regression and interpolation. Platform: |
Size: 691200 |
Author:大木木 |
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Description: Wavelet Texture classification example.
Features created using wavelet transform,and classified by A-Wavelet Texture classification example.
Features created using wavelet transform,and classified by ANN Platform: |
Size: 880640 |
Author:bergen |
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Description: The early detection of arrhythmia is very important
for the cardiac patients. This done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data.-The early detection of arrhythmia is very important
for the cardiac patients. This is done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data. Platform: |
Size: 273408 |
Author:Amit Majumder |
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Search - ANN classification