Description: Principal component analysis,for study about classification data,develop for svm , lvq etc-Principal component analysis,for study about classification data,develop for svm , lvq etc Platform: |
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Author:wichitra |
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Description: This project compares the performance of SOM versus LVQ in classification problems.
Given two data sets:
‘iris.dat’ has 150 patterns of 3 classes with 4 features.
‘wine.dat’ has 178 patterns of 3 classes with 13 features.
For SOM, use its algorithm (not use MATLAB tool), but for LVQ use MATLAB tool. Platform: |
Size: 8192 |
Author:massumeh |
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Description: 对手写体汉字识别在特征提取、分类识别及后处理三个阶段主要采用的方法做了简要介绍。SVM 具有结构简
单, 分类稳定可靠, 且容错性好等优点。同时和LVQ 神经网络分类器识别方法进行了比较, 计算机仿真表明, 采用SVM 用
于手写体汉字识别更适合-Handwritten Chinese character recognition for feature extraction, classification and recognition and post-processing methods used three main stages made a brief presentation. SVM has a simple structure, classification stable and reliable, and fault tolerance and good. Simultaneously and LVQ neural network classifier identification methods were compared, computer simulations show that using SVM for handwritten Chinese character recognition is more suitable Platform: |
Size: 137216 |
Author:xj |
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Description: LVQ神经网络，此种方法适合于两分类结果的数据处理-LVQ ANN。This method is suitable for the data processing of the two classification Platform: |
Size: 1024 |
Author:zzwwrr |
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Description: Abstract— Identifying exceptional students for
scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and
can be used as an effective alternative system for classifying students. -Abstract— Identifying exceptional students for
scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and
can be used as an effective alternative system for classifying students. Platform: |
Size: 299008 |
Author:Nguyen Anh Tuan |
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Search - lvq for classification