Name
Abstract | ||
|---|---|---|
Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICDM.2006.34 | ICDM |
Keywords | Field | DocType |
support vector machines,empirical study,gaussian kernel,gaussian processes,hierarchical clustering | Data mining,Data set,Computer science,Artificial intelligence,Gaussian process,Gaussian function,Hierarchical clustering,Kernel (linear algebra),Pattern recognition,Support vector machine,Sampling (statistics),Machine learning,Scalability | Conference |
ISSN | ISBN | Citations |
1550-4786 | 0-7695-2701-9 | 5 |
PageRank | References | Authors |
0.63 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Asharaf | 1 | 122 | 13.07 |
| M. Narasimha Murty | 2 | 824 | 86.07 |
| Shirish Krishnaj Shevade | 3 | 285 | 28.53 |