Name
Title | ||
|---|---|---|
Preselection of support vector candidates by relative neighborhood graph for large-scale character recognition |
Abstract | ||
|---|---|---|
We propose a pre-selection method for training support vector machines (SVM) with a large-scale dataset. Specifically, the proposed method selects patterns around the class boundary and the selected data is fed to train an SVM. For the selection, that is, searching for boundary patterns, we utilize a relative neighborhood graph (RNG). An RNG has an edge for each pair of neighboring patterns and thus, we can find boundary patterns by looking for edges connecting patterns from different classes. Through large-scale handwritten digit pattern recognition experiments, we show that the proposed pre-selection method accelerates SVM training process 5–15 times faster without degrading recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDAR.2015.7333773 | International Conference on Document Analysis and Recognition |
Keywords | Field | DocType |
large-scale character recognition,support vector machine training,large-scale dataset,class boundary,boundary patterns,RNG,neighboring patterns,large-scale handwritten digit pattern recognition experiments,SVM training process,relative neighborhood graph,support vector candidate preselection method | Character recognition,Pattern recognition,Computer science,Relative neighborhood graph,Support vector machine,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
1520-5363 | 3 | 0.40 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masanori Goto | 1 | 8 | 0.85 |
| Ryosuke Ishida | 2 | 3 | 0.40 |
| Seiichi Uchida | 3 | 790 | 105.59 |