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.
Subject-Independent Facial Expression Recognition with Biologically Inspired Features
Weifeng Liu, Caifeng Song, Yanjiang Wang College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, P.R. China
Abstract—Despite of much research for facial expression recognition, recognizing facial expressions across different persons is still a challenging computer vision task. However, facial expression analysis seems naturally for human visual system. Motivated by visual biology, this paper proposes an invariant feature extraction method for subject-independent facial expression recognition. In particular, we extract the biologically inspired facial features using extended visual cortex model-HMAX which consist of a template matching and a maximum pooling operation. We carefully organized the facial features and achieve subject-independent facial expression recognition using a sparse representation based classifier. The experiments on Yale database and JAFFE database demonstrate the significance of our proposed method for subject-independent facial expression recognition.
Cellular Differentiation Algorithm for High Dimensional Numerical Function Optimization
Yanjiang Wang，Chengna Yuan , Weifeng Liu
College of Information and Control Engineering, China University of Petroleum (East China)Qingdao, P.R.China
Abstract— Inspired by the cellular differentiation mechanism of organisms, combined with the theory of artificial life and swarm intelligence, a new biomimetic optimization algorithm, cellular differentiation optimization algorithm (CDOA), is proposed in this paper. A certain number of cells are randomly distributed in the search space to find the optimal solution by activating their differential behaviors such as division, growth, migration, adhesion and apoptosis. Experimental results on several benchmark complex functions with high dimensions show that the proposed cellular differentiation optimization algorithm can rapidly converge at high quality solutions and outperform some of the state-of-art in high-dimension numerical function optimization.