The strong convergence of visual classification method and its applications in disease diagnosis

Visual classification method is introduced as a learning strategy for pattern classification problem in bioinformatics. In this paper, we show the strong convergence property of the proposed method. In particular, the method is shown to converge to the Bayes estimator, i.e., the learning error of the method tends to achieve the posterior expected minimal value. The method is successfully applied to some practical disease diagnosis problems. The experimental results all verify the validity and effectiveness of the theoretical conclusions. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.