Description: 云分类器，一种基于云模型理论的分类程序。程序中使用云分类器对Iris数据集进行了测试-cloud classifier, a theoretical model based on cloud classification procedure. Procedures used for cloud classification of Iris data set for testing Platform: |
Size: 3373 |
Author:王力 |
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Description: 云分类器，一种基于云模型理论的分类程序。程序中使用云分类器对Iris数据集进行了测试-cloud classifier, a theoretical model based on cloud classification procedure. Procedures used for cloud classification of Iris data set for testing Platform: |
Size: 3072 |
Author:王力 |
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Description: C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描和排序，因而导致算法的低效。此外，C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行。-C4.5 algorithm has the following advantages: the classification rules easier to understand, accurate and a higher rate. Its shortcomings are as follows: in the tree structure, the need for a number of data sets the order of scanning and sorting, thus leading to inefficient algorithms. In addition, C4.5 can only be applied to the presence of a data set in memory, when the training set too great to accommodate in memory when the program can not run. Platform: |
Size: 1024 |
Author:xinyuanwo |
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Description: 一个比较简单的模式识别问题。用female.txt 和male.txt 的数据作为训练样本集，建立Bayes 分类器，用测试样本数据set1.txt、set2.txt、set3.txt 对该分类器进行测试,分别应用单个特征及两个特征进行实验-A relatively simple pattern recognition problem. Female.txt and male.txt use data as a training sample set, the establishment of Bayes classifier, using the test sample data set1.txt, set2.txt, set3.txt to test the classifier, respectively, the application of individual characteristics and two characteristics experiment Platform: |
Size: 3072 |
Author:晏珏 |
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Description: Fisher线性判别分类器应用于IRIS数据集的例子-An example of Fisher linear discriminant Classifier applied on IRIS Data Set Platform: |
Size: 164864 |
Author:LikeTheBird |
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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: Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM) - originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
-Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM)- originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
Platform: |
Size: 214016 |
Author:Jessie |
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Description: 以黎曼几何为理论依据，基于S.Amari的修正核函数思想提出了两种新的保角变换，用其对核函数进行数据依赖性改进，进一步提高支持向量机分类器泛化能力。以人工非线性分类问题
为对象进行研究，仿真实验结果表明采用新保角映射可以快速显著地改善分类器泛化性能，而且能大幅度地减少支持向量的数目。-Two novel conformal transformations were proposed based on the Riemannian geometry theory and S.Amari’sidea.And the kernel function was modified by the transformation in a data-dependent way.Our experimental results for theartificial nonlinear data set show that the generalization performance of support vector machines classifier is improvedremarkably and the number of support vectors is decreased greatly. Platform: |
Size: 671744 |
Author:liuqian |
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Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. Platform: |
Size: 21504 |
Author:mhm |
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Description: 根据贝叶斯原理设计的一个简单的分类器，利用已知样本数据训练后，分类器就可以对未知样本进行分类。（实验时采用的是Iris数据集。）-According to the design of a simple Bayesian classifier, using the known training sample data, the classifier can classify the unknown samples. (Experiments using the Iris data set.) Platform: |
Size: 9216 |
Author:lhb |
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Description: this a program that combine knn classifier with PCA to classify vehicle data set.-this is a program that combine knn classifier with PCA to classify vehicle data set. Platform: |
Size: 2048 |
Author:paolo |
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Description: Iris数据集的分类程序，包括线性分类器实验，BP网络分类器实验，以及异或数据的BP网络分类实验，外带试验报告-Iris data set of classification procedures, including linear classification experiment, BP network classifier experiments, and different BP networks or data classification experiment, take-test report Platform: |
Size: 623616 |
Author:刘文 |
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Description: Bayes分类器——算法设计
1． 使用决策树(Decision tree)分类算法、朴素贝叶斯（Naï ve Bayes）算法或者K-近邻（kNN）算法（三者任选其一）对给定的训练数据集构造分类器，并在测试数据集上进行分类预测。
2． 数据集描述：
Tic-tac-toe游戏的二叉分类。Tic-tac-toe游戏示例如下-Bayes classifier- Algorithm 1. Using the decision tree (Decision tree) classification algorithm, Naive Bayes (Naï ve Bayes) algorithm or K-nearest neighbor (kNN) algorithm (choose any one of three) on a given set of training data classification structure, and the test data Classification and Prediction on the set. 2. Data set description: Tic-tac-toe game binary classification. Tic-tac-toe game example is as follows Platform: |
Size: 1439744 |
Author:vera |
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Description: a good algorithm for classification performance improvement (use PCA and LDA respectively and then implement the resulted data set into your classifier) Platform: |
Size: 1024 |
Author:nazila |
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Description: ANNbp对UCI中鸢尾花数据集分类，150做训练样本，剩下150做测试样本-ANNbp on the UCI iris data set classification, 150 training samples, the remaining 150 test samples Platform: |
Size: 561152 |
Author:洛霜 |
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Description: 用matlab编程实现随机球覆盖集成分类器算法，首先在UCI数据集上得到很好的结果，然后在6个基因表达数据集通过一个案例说明。-Implement random ball cover integrated classifier algorithm using matlab programming, first get good results on UCI data set, and then in six gene expression data sets through a case description. Platform: |
Size: 877568 |
Author:lubo |
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Search - classifier data set