CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE.
CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE.
复制标题
鱼子酱：通过聚合回归进行分类及其在绿洲大脑数据库分类中的应用。
DOI：
10.1109/isbi.2010.5490244
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Vemuri,BabaC
中科院分区：
文献类型：
--
作者：
Chen,Ting;Rangarajan,Anand;Vemuri,BabaC
This paper presents a novel classification via aggregated regression algorithm - dubbed CAVIAR - and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using the nearest neighbor method is imposed in the testing stage to avoid overfitting. A closed form solution to the cost function is derived for this algorithm. We use a novel feature - the histogram of the deformation field between the MRI brain scan and the atlas which captures the structural changes in the scan with respect to the atlas brain - and this allows us to automatically discriminate between various classes within OASIS using CAVIAR. We empirically show that CAVIAR significantly increases the performance of the weak classifiers by showcasing the performance of our technique on OASIS.
