TY - GEN
T1 - Boosting with spatial regularization
AU - Xiang, Zhen James
AU - Xi, Yongxin Taylor
AU - Hasson, Uri
AU - Ramadge, Peter Jeffrey
PY - 2009
Y1 - 2009
N2 - By adding a spatial regularization kernel to a standard loss function formulation of the boosting problem, we develop a framework for spatially informed boosting. From this regularized loss framework we derive an efficient boosting algorithm that uses additional weights/priors on the base classifiers. We prove that the proposed algorithm exhibits a "grouping effect", which encourages the selection of all spatially local, discriminative base classifiers. The algorithm's primary advantage is in applications where the trained classifier is used to identify the spatial pattern of discriminative information, e.g. the voxel selection problem in fMRI. We demonstrate the algorithm's performance on various data sets.
AB - By adding a spatial regularization kernel to a standard loss function formulation of the boosting problem, we develop a framework for spatially informed boosting. From this regularized loss framework we derive an efficient boosting algorithm that uses additional weights/priors on the base classifiers. We prove that the proposed algorithm exhibits a "grouping effect", which encourages the selection of all spatially local, discriminative base classifiers. The algorithm's primary advantage is in applications where the trained classifier is used to identify the spatial pattern of discriminative information, e.g. the voxel selection problem in fMRI. We demonstrate the algorithm's performance on various data sets.
UR - http://www.scopus.com/inward/record.url?scp=78149473368&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:78149473368
SN - 9781615679119
T3 - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
SP - 2107
EP - 2115
BT - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
PB - Neural Information Processing Systems
T2 - 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
Y2 - 7 December 2009 through 10 December 2009
ER -