Thank you very much for your support for ICCH2012 (The 2012 International Conference on Computerized Healthcare) which is to be held in Hong Kong during December 17-18, 2012. We greatly appreciate your time and effort for the paper submission to ICCH2012 Special Session on Machine Learning for Health Informatics. On behalf of ICCH2012 organization committee, I am pleased to inform you that your paper (Paper ID #: MLHI_0106) has been accepted for publication. Congratulations!
Laplacian Support Vector Machines for Medical Diagnosis
Caifeng Song, Weifeng Liu, Yanjiang Wang
College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, P.R.China
Abstract—A semi-supervised learning method is presented for medical diagnosis owing to the large amount of unlabeled samples of training model. Laplacian graph which is state-of-the-art method in manifold regularization is used to smooth the probability density functions. The Laplacian regularization term is added to SVM algorithm constituted LapSVM which would be applied to medical data classification and verified on Breast Cancer Dataset, Mammographic Mass Dataset and Thyroid Gland Dataset. Experiments indicate that LapSVM can achieve a better performance using the small labeled samples and large unlabeled samples.