集成技术Issue(6)：27-30,4.
多模态集成阿尔茨海默病和轻度认知障碍分类
Multimodal Ensemble Classiifcation of Alzheimer’s Disease and Mild Cognitive Impairment
摘要
Abstract
To effectively diagnose Alzheimer’s disease (AD) and mild cognitive impairment (MCI), a multimodal ensemble support vector machine (SVM) based on multi-modality data was proposed and used for the classiifcation of AD and MCI. The ensemble learning was employed and the discrimination information of classiifcation was extracted from different multiple modalities data, then the SVM was used for classiifcation of AD and MCI. In order to validate the efifcacy of proposed method, a 10-fold cross-validation was used and tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method is better than multi-modality linear multiple kernel learning and single-modality method.关键词
阿尔茨海默病/轻微认知障碍/多模态集成学习/支持向量机Key words
Alzheimer’s disease/mild cognitive impairment/multimodal ensemble learning/support vector machine引用本文复制引用
程波,钟静,熊江..多模态集成阿尔茨海默病和轻度认知障碍分类[J].集成技术,2013,(6):27-30,4.基金项目
重庆市教委科学技术研究项目(No. KJ121111和KJ131108)。 （No. KJ121111和KJ131108）
