Description: 在图像处理中经常涉及分类，多次有用到非参数估计。本程序用Parzen窗来估计，用随机高斯函数来作计策信号.-in image processing involves classification, the many useful to the non - parametric estimation. The procedure used to estimate Parzen window, using random Gaussian function to signal for the ploy. Platform: |
Size: 58218 |
Author:江佳 |
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Description: 在图像处理中经常涉及分类，多次有用到非参数估计。本程序用Parzen窗来估计，用随机高斯函数来作计策信号.-in image processing involves classification, the many useful to the non- parametric estimation. The procedure used to estimate Parzen window, using random Gaussian function to signal for the ploy. Platform: |
Size: 58368 |
Author: |
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Description: 这是一个parzen窗口分类算法，希望能给大家的学习有所帮助-This is a Parzen window classification algorithm, we hope that they will be helpful to the learning Platform: |
Size: 2048 |
Author:王霞 |
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Description: ddtool,实现one class classification.包括gaussian 模型， gaussian 混合模型，Parzen density，knn,kmean,kcenter等方法-ddtool, the realization of one class classification. including the Gaussian model, gaussian mixture model, Parzen density, knn, kmean, kcenter methods such as Platform: |
Size: 439296 |
Author:何威 |
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Description: 用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类，Bayesian分类，Parzen窗，KNN]和特征选择和降维方法[PCA,LDA]进行了模拟，并给出了各类分类方法的结果，-It s the simulations about linear classification ,Bayesian ,Parzen and KNN of pattern recognition .And ,It gives the results. Platform: |
Size: 24576 |
Author:yuanna |
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Description: 这是一个模式识别中的parzen窗的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition in a simple window parzen Category simulation examples, one of female.txt and male.txt training samples, test.txt is the measurement, classification effect is very good for beginners will be learning model has very big help. Platform: |
Size: 2048 |
Author:刘锐 |
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Description: 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 the decision rules to identify with the ability of automatic classification. This paper introduces the Parzen window method using a random pattern classifier, and achieved a simple matlab random pattern classifier. Platform: |
Size: 287744 |
Author:丑力 |
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Description: Parzen概率神经网络工具箱及代码实现分类，供学习应用者参考。-Parzen Probabilistic Neural network toolbox and code to achieve the classification for the study and application in Taiwan. Platform: |
Size: 6144 |
Author:zangtianlei |
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Description: 本实验的目的是了解非参数估计--parzen窗估计，对数据进行Parzen窗估计和设计分类器。并分析窗口宽度h对分类的影响。-The purpose of this study is to understand the non-parametric estimation- parzen window estimation, Parzen window estimate of the data and design of classifiers. Window width h and analyzed the impact on the classification. Platform: |
Size: 2048 |
Author:林耀明 |
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Description: 用parzen来计算所选的数据的概率密度函数，所选的窗函数是方窗，最后基于最小错误率的贝叶斯进行分类-With parzen selected data to calculate the probability density function, the selected window function is the side window, and finally the smallest error rate based on Bayesian classification Platform: |
Size: 4096 |
Author:王爱华 |
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Description: 用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与在测试集上得到的错误率进行比较。-Use Parzen Windows method or the kn neighbor method estimated probability density function, draw the bayes classifier, to test sample to test, and the parameter estimation is based on the classifier and get the difference of classification performance. 2. At the same time the height and weight data as the characteristic, with Fisher identification method for linear classifier, and the application of the classifier to training and test sample, investigation and test training error condition. Will the training sample and utility of decision on the drawing boundary paint, and at the same time before the Bayes method with optimal classifier also paint chart, the result of the comparison of the similarities and differences. 3. Select the above or before any a kind of method of experiment, with a method for training set in the estimation error rates, and on the test set get error rates are compared. Platform: |
Size: 202752 |
Author:QUAN RONG |
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Description: 基于parzen窗口的贝叶斯分类，先对数据进行训练，再对测试数据进行识别。-Bayesian classification based on parzen window of data, training, and identification of the test data. Platform: |
Size: 1024 |
Author: |
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Description: 老师给的模式识别的编程例子，包括bayes分类算法、支持向量机分类、Parzen窗估计等算法，程序简单明了，是不错的例子。-Pattern Recognition teacher programming examples, including bayes classification algorithm, support vector machine classification, Parzen windows estimation algorithm, the procedure is simple, is a good example. Platform: |
Size: 5120 |
Author:李程序员 |
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Description: Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in a poor accuracy of classification. To seek an efficient
solution, this paper presents a non-parametric and variational
multiple level set model by a joint use of Aerial image and two
products, digital terrain model (DTM) and digital surface model
(DSM), directly or indirectly derived raw LiDAR (Light
Detection And Ranging) 3D point cloud data. Proposed model is
to minimize an energy function. The energy includes two terms.
First term is mainly image-based energy which introduces Parzen
Window density estimation technique in the multiple level set
framework. To make up the disadvantages-Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in a poor accuracy of classification. To seek an efficient
solution, this paper presents a non-parametric and variational
multiple level set model by a joint use of Aerial image and two
products, digital terrain model (DTM) and digital surface model
(DSM), directly or indirectly derived raw LiDAR (Light
Detection And Ranging) 3D point cloud data. Proposed model is
to minimize an energy function. The energy includes two terms.
First term is mainly image-based energy which introduces Parzen
Window density estimation technique in the multiple level set
framework. To make up the disadvantages Platform: |
Size: 2544640 |
Author:yangs |
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Description: 模式识别的一些基本的算法，有最大最小距离分类法，简单的感知器算法，LMSE算法，以及Parzen窗函数。-Pattern recognition of some basic algorithms, have the largest minimum distance classification, simple perceptron algorithm, LMSE algorithm, as well as the Parzen window function. Platform: |
Size: 3072 |
Author:唐裕亮 |
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Search - Parzen classification