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用pca分类器将Iris数据分开,并分析pca分类器的特点-Classifier with the Iris data pca separate and analyze the characteristics of classification pca
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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 typ
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完整的pca小程序,简单的一些数据。进行分类。是模式识别的基础的一些东西-Complete pca applet, simply some of the data. For classification. Pattern recognition is based on something
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主分量分析,用于对脑电信号的判别分析,也可用于其他的二分类分析。-the classifier of principle component analysis,using for EEG and binary classification
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一些典型的模式分类及聚类方法 包括k均值 FDA PCA LMS 贝叶斯 K近邻-The typical pattern classification and clustering methods including k-means FDA PCA LMS Bayesian K-nearest neighbor
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pca主成分分析法,能够对高维数据进行降维,并能够实现分类-pca principal component analysis to reduce the dimensionality of high-dimensional data, and be able to achieve the classification
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实用主成分分析和人工神经网络方法,对数据进行处理分析-Classification Using PCA and FFT
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该算法主要是模式识别的分类算法,KNN 最近邻分类算法-The algorithm is pattern recognition classification algorithm, KNN nearest neighbor classification algorithm
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心电图是一种经胸腔的以时间为单位记录心脏的电生理活动,并通过皮肤上的电极捕捉并记录下来的诊疗技术,能警示人体心脏健康状况。我们对十种不同心脏状况的心电图(ECG)数据进行实验,在对原始数据进行了一系列预处理后,利用PCA和ICA进行特征提取降维得到新的特征空间,并利用支持向量机进行训练,最后对未分类心电数据进行分类标记并评价其性能。-Electrocardiography (ECG) is a transthoracic interpretation of the electrical acti
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PCA distance and classification example
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利用PCA-SVM的图片降维和识别分类,并分析重建误差等的主程序-The use of PCA-SVM pictures dimensionality reduction and identification and classification, and analyze the main reconstruction error, etc.
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PCA算法实现对数据降维,train_sample为训练样本,train_class为训练样本的分类结果,test_sample为测试样本,test_class为测试样本的分类结果,可以从UCI下载数据集进行调用~-PCA algorithm for data dimensionality reduction, train_sample of training samples, train_class for the classification of training samples, tes
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这是一个包含PCA降维的matlab程序加上用svm分类的一个程序。-This is a PCA dimensionality reduction plus matlab program contains a program with svm classification.
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使用主成分分析的方法进行英文字母识别,使用了多种限制条件的协方差矩阵进行测试-use PCA for alphabet classification
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又提供一基于生理的情感神经网络,包含杏仁体,丘脑,视觉神经等重要元素,可用于分类与预测,性能优越于SVM,BP-The use of neural network based on emotional physiology, contains important elements of the amygdala, thalamus, optic nerve, etc., can be used for classification and prediction, superior perform
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用于各种特征提取主成分分析法,如表情提取,人脸提取,识别分类等-For a variety of features to extract the principal component analysis (pca),Such as expression extraction, face extraction, identification classification, etc
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pca主成分分析,在多变量选择上效果较好,对数据的主成分进行分析,常用于分类、聚类、实验数据处理-Pca principal component analysis in multivariate selection effect is good, principal component analysis of data, often used in classification, clustering, experimental data processing
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此代码是采用PCA进行分类,神经网络进行自动识别,是自己手动编写的线性代码,有什么不对的地方,多多交流。-This code is the use of PCA classification, neural networks for automatic identification, is to manually write linear code, what is wrong with it, more exchanges.
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This the code for PCA (Principle component analysis). First PCA is used for dimension reduction. And then KNN is applied for classification.-This is the code for PCA (Principle component analysis). First PCA is used for dimension reduction. And then
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没有使用matlab自带工具包,而是使用了自写的PCA,来用其对数据进行线性分类。-without using the toolbox given by matlab,i write pca myself and implement it into LDA classification.
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