Description: 模式识别分类器的设计，此为K均值法源码，经调试通过。所用数据为标准IRIS。-Pattern recognition classifier design, the source for the K-means, after debugging through. Data used as the standard IRIS. Platform: |
Size: 1024 |
Author:leestar |
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Description: 模糊K近邻分类器，模式识别和机器学习经常要使用的一个分类器-K neighbor fuzzy classifier, pattern recognition and machine learning are often used in a classifier Platform: |
Size: 429056 |
Author:chen |
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Description: ：将K—means算法引入到朴素贝叶斯分类研究中，提出一种基于K—means的朴素贝叶斯分类算法。首先用K—
me．arks算法对原始数据集中的完整数据子集进行聚类，计算缺失数据子集中的每条记录与 个簇重心之间的相似度，把记
录赋给距离最近的一个簇，并用该簇相应的属性均值来填充记录的缺失值，然后用朴素贝叶斯分类算法对处理后的数据
集进行分类。实验结果表明，与朴素贝叶斯相比，基于K—means思想的朴素贝叶斯算法具有较高的分类准确率。-: K-means algorithm will be introduced to the Naive Bayesian Classifier study, a K-means based on the Naive Bayesian classification algorithm. First of all, with K-me. arks algorithm focus on the raw data of the complete data subset of the cluster, the calculation of missing data for each subset of records and the similarity between the cluster center of gravity to the nearest record assigned to a cluster, and the corresponding attributes of the cluster means to fill the missing value record, and then use Naive Bayes classification algorithm to deal with the data set after classification. The experimental results show that compared with the Naive Bayes, K-means based on the thinking of Naive Bayes algorithm has higher classification accuracy. Platform: |
Size: 173056 |
Author:李浩 |
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Description: K-means分类器，java实现，可以自己设定类别数目。已做成jar-K-means classifier, java implementation, you can set the number of categories. Has made jar Platform: |
Size: 53248 |
Author:achilles |
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Description: Graphic Detection and Recognition using Bag-of-word method, finding feature vector with K-means classifier, training or testing data with Naive Bayes Classifier or PLCA method. Platform: |
Size: 32281600 |
Author:Huang Hua |
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Description: 2类分类高斯混合模型
使用k-means的方法来初始化GMM，
基于EM算法计算出GMM模型参量。
测试GMM模型分别有2个，4个，8个混合成分-2-class classifier with Gaussian Mixture Models. Use the k-means method to initialize the GMM’s
Then improve the GMM models
iteratively based on the EM algo-rithm.
Investigate and report results for GMM s which have 2, 4, 8 mixture components respectively. Platform: |
Size: 3072 |
Author:王沛霖 |
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Description: 图像场景分类的bow模型opencv源代码，采用k-means聚类构造单词，采用支持向量机的svm分类器。-Image scene classification bow model opencv source code, using k-means clustering structure of words, using support vector machine svm classifier. Platform: |
Size: 10240 |
Author:李翔 |
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Description: In this era an emerging filed in the data mining is data stream mining. The current research technique of the data stream is classification which mainly focuses on concept drift data. In mining drift data with the single classifier is not sufficient for classifying the data. Because of the high dimensionality and does not get processed within considerable time, memory, false alarm rate is high, classification accuracy result is low. In this paper, proposed a Genetic based Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. The proposed method achieves improvement in classification accuracy and perhaps to select the least number of features which show the way to simplification of learning task. The experimental shows that the advocated method performs well when compared with existing methods. Platform: |
Size: 107520 |
Author:Opencvresearcher |
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Description: 针对目前传统的枸杞分级主要采用人工方法， 费时费力且效率不高的缺点， 提出了一种基于机器视觉技术对枸杞
进行自动分类的方法。 采用数字图像处理技术对枸杞图像进行了预处理、 分割 ， 从而提取枸杞的色泽、 大小及形状等特征
参数； 用 K-means 算法对特征进行聚类， 得到枸杞相应等级的基准； 根据聚类分析得到的基准采用最小距离分类器对枸杞
进行分级。 实验结果表明 ， 该方法能够准确快速地对不同色泽和大小的枸杞进行分类。-Traditional wolfberry sorting primarily uses artificial method. It has time-consuming and inefficient shortcomings. An
automatic wolfberry classification method based on machine vision is proposed. This paper uses digital image processing technology for wolfberry image pre-processing, segmentation and extraction of characteristic parameters of color, size and shape it
uses the K-means clustering feature to get the baseline of wolfberry appropriate level it grades wolfberry by minimum distance
classifier based on the trained benchmark. The experimental results show that this method can classify different colors and sizes
of wolfberry more accurately and quickly. Platform: |
Size: 1451008 |
Author:李祥龙 |
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