Tree-structured support vector machine with confusion cross for complex pattern recognition problems

Support vector machines (SVMs) have gained wide acceptance because of the high generalization ability for a wide range of pattern recognition problems. We address problems associated with complex pattern recognition in this paper and present a tree-structured support vector machine (TSSVM) with confusion cross. A TSSVM is overall a binary tree, whose internal nodes are modular SVMs. Those two non-terminal nodes generated from the same parent node perform discounted confusion crossover. The presented approach is evaluated against other classifiers investigated lately. The performance of the proposed approach is demonstrated with some typical complex classification problems.