Name
Abstract | ||
|---|---|---|
Support vector machine (SVM) has been successfully applied to solve a large number of classification problems. Despite its good theoretic foundations and good capability of generalization, it is a big challenging task for the large data sets due to the training complexity, high memory requirements and slow convergence. In this paper, we present a new method, SVM classification based on fuzzy clustering. Before applying SVM we use fuzzy clustering, in this stage the optimal number of clusters are not needed in order to have less computational cost. We only need to partition the training data set briefly. The SVM classification is realized with the center of the groups. Then the de-clustering and SVM classification via reduced data are used. The proposed approach is scalable to large data sets with high classification accuracy and fast convergence speed. Empirical studies show that the proposed approach achieves good performance for large data sets. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11925231_54 | MICAI |
Keywords | Field | DocType |
training data,reduced data,large data,fuzzy clustering,large data set,large number,classification problem,support vector machine classification,high classification accuracy,svm classification,empirical study,support vector machine | Convergence (routing),Structured support vector machine,Fuzzy clustering,Data mining,Data set,High memory,Computer science,Support vector machine,Fuzzy logic,Artificial intelligence,Machine learning,Scalability | Conference |
Volume | ISSN | ISBN |
4293 | 0302-9743 | 3-540-49026-4 |
Citations | PageRank | References |
16 | 0.87 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jair Cervantes | 1 | 176 | 18.08 |
| Xiaoou Li | 2 | 550 | 61.95 |
| Wen Yu | 3 | 283 | 22.70 |