Name
Abstract | ||
|---|---|---|
Normal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel SVM classification approach for large data sets. It has two phases. In the first phase, an approximate classification is obtained by SVM using fast clustering techniques to select the training data from the original data set. In the second phase, the classification is refined by using only data near to the approximate hyper plane obtained in the first phase. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers. The proposed classifier has distinctive advantages on dealing with huge data sets. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/GrC.2010.46 | GrC |
Keywords | Field | DocType |
svm classification method,pattern clustering,large data set classification,large data sets,approximate classification method,pattern classification,high training complexity,good classification accuracy,large data set,svm classifier,huge data set,novel svm classification approach,support vector machine algorithms,approximate hyper plane,approximate classification,novel svm classification method,fast clustering techniques,support vector machines,original data,training data,kernel,optimization,support vector machine,clustering algorithms,accuracy | Structured support vector machine,Data mining,Data set,Computer science,Artificial intelligence,Hyperplane,Cluster analysis,Classifier (linguistics),Training set,Kernel (linear algebra),Pattern recognition,Support vector machine,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-7964-1 | 4 | 0.38 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoou Li | 1 | 550 | 61.95 |
| Jair Cervantes | 2 | 176 | 18.08 |
| Wen Yu | 3 | 381 | 39.20 |