Linearized and kernelized sparse multitask learning for predicting cognitive outcomes in Alzheimer's disease

DOI10.1155/2018/7429782zbMath1409.92117DBLPjournals/cmmm/LiuCYZ18OpenAlexW2787016523WikidataQ55085408 ScholiaQ55085408MaRDI QIDQ1731300

Dazhe Zhao, Jinzhu Yang, Xiaoli Liu, Peng Cao

Publication date: 13 March 2019

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/7429782

zbMATH Keywords

Alzheimer's diseasecognitive outcome predictionMRI features

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55) Medical applications (general) (92C50) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (1)

Feature selection and classification for high-dimensional incomplete multimodal data

Cites Work

• Convex multi-task feature learning
• Localized algorithms for multiple kernel learning
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