Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency.
|Number of pages||6|
|Journal||Expert Systems With Applications|
|Publication status||Published - 2008 Apr|
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence