Neighborhood Property-Based Pattern Selection for Support Vector Machines

Support Vector Machine (SVM) has been spotlighted in the machine learning community thanks to its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training. 1 

• Type: Journal
• Year: 2007
• Where: NECO
• Authors: Hyunjung Shin, Sungzoon Cho
• Keywords: Community Thanks, NECO 2007, Support Vector Machine, Theoretical Soundness