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Title: Behavior-Constrained Support Vector Machines for fMRI Data Analysis
Author: Chen, Danmei ; Li, Sheng ; Kourtzi, Zoe ; Wu, Si
Source: IEEE TRANSACTIONS ON NEURAL NETWORKS
Issued Date: 2010
Volume: 21, Issue:10, Pages:1680-1685
Keyword: Functional magnetic resonance imaging (fMRI) ; pattern classification ; psychometric function ; support vector machine (SVM) ; HUMAN VISUAL-CORTEX ; PREFRONTAL CORTEX ; HUMAN BRAIN ; CLASSIFICATION ; SHAPES ; MODEL
Subject: Computer Science ; Engineering
Corresponding Author: Wu, S (reprint author), Chinese Acad Sci, Lab Neural Informat Proc, Inst Neurosci, Shanghai 200031, Peoples R China,siwu@ion.ac.cn
English Abstract: Statistical learning methods are emerging as a valuable tool for decoding information from neural imaging data. The noisy signal and the limited number of training patterns that are typically recorded from functional brain imaging experiments pose a challenge for the application of statistical learning methods in the analysis of brain data. To overcome this difficulty, we propose using prior knowledge based on the behavioral performance of human observers to enhance the training of support vector machines (SVMs). We collect behavioral responses from human observers performing a categorization task during functional magnetic resonance imaging scanning. We use the psychometric function generated based on the observers behavioral choices as a distance constraint for training an SVM. We call this method behavior-constrained SVM (BCSVM). Our findings confirm that BCSVM outperforms SVM consistently.
Indexed Type: sci
Language: 英语
Content Type: 期刊论文
URI: http://ir.sibs.ac.cn/handle/331001/1570
Appears in Collections:神经所(总)_期刊论文

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Recommended Citation:
Chen, Danmei; Li, Sheng; Kourtzi, Zoe; Wu, Si.Behavior-Constrained Support Vector Machines for fMRI Data Analysis,IEEE TRANSACTIONS ON NEURAL NETWORKS,2010,21(10):1680-1685
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