Titlebook: Learning Discourse; Discursive approache Carolyn Kieran,Ellice Forman,Anna Sfard Book 2002 Springer Science+Business Media Dordrecht 2002 A

尊重

Anna Sfard reconstruction problem into a general framework. Further, it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. Since .. filter seeks minimum-maximum-error estimates without any assumptions on the system and data noise statis

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Bert van Oers To evaluate the accuracy, we performed extensive experiments on synthetic data simulating the progression of Alzheimer’s disease. Subsequently, the method was applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data to estimate the central event ordering in the dataset. The experiment

GORGE

Stephen Lermanlti-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an . longitudinal brain image

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M. C. O’Connoress these limitations, we propose a novel dynamic hyper-graph inference framework, working in a semi-supervised manner, which iteratively estimates and adjusts the subject-wise relationship from multi-modal neuroimaging data until the learned data representation (encoded in the hyper-graph) achieves

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陶瓷

Vicki Zack,Barbara Graves (like the mean) can be computed, but efficient alternatives are helpful in speeding up algorithms that use means iteratively, like hypothesis testing. In this paper, we take advantage of a very large dataset (. = 8016) to obtain computable approximations, under the assumption that the data trees pa

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Trypsin

Falk Seeger for the image which incorporates a “line process” to model the presence of discontinuities. In the case where no . edge information is available, this line process may be estimated directly from the data. When edges are available from MR images, this information is introduced as a set of known . li

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