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MATLAB编写的误差反向传播（BP）神经网络简单分类器。-MATLAB prepared by error back-propagation [BP] neural network classifier easy.
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采用身高和体重数据作为特征，分别假设二者相关或不相关，在正态分布假设下估计概率密度，建立最小错误率Bayes分类器，写出得到的决策规则，将该分类器应用到训练/测试样本，考察训练/测试错误情况。-The use of height and weight data as the characteristics of the assumption that the two were related or not related to the normal distribution probabilit
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分类器的训练与学习是模式识别的一个重要环节,其目的在于按照某种算法,确定判决规则,使之具有自动分类识别的能力。本文介绍了采用Parzen窗法的随机模式分类器,并matlab实现了一个简易的随机模式分类器。-Classifier training and learning is an important part of pattern recognition, in accordance with the purpose of some kind of algorithm to determine
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贝叶斯分类器的分类原理是通过某对象的先验概率，本文详细介绍贝叶斯分类器，使用贝叶斯分类器对样本进行训练分类，得到良好分类结果，并对分类结果进行分析。-Principle of Bayesian classifiers is through prior probability of an object, the paper describes Bayesian classifier, Bayesian classifier using the training sample classificat
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This is the source code for a Single Vector Machine classifier, briefly called a SVM classifier.
There are 60 peices assigned for training and 30 for testing the classifier.
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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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经典的Adaboost分类器训练过程详细的matlab代码解释，对做模式识别的人很有用-Classic Adaboost classifier training process explained in detail matlab code, to do pattern recognition is useful
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在MATLAB中利用贝叶斯分类器对训练数据进行分类，然后利用测试数据对贝叶斯分类器的效果进行测试-The training data are classified using the Bayesian classifier in MATLAB, then use the data to test the effect of Bayesian classifier test
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概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(The rate neural network, first proposed in 1989, is a branch of the RBF network and is one of the fe
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