Global ETD Search

1	Insights and Characterization of l1-norm Based Sparsity Learning of a Lexicographically Encoded Capacity Vector for the Choquet Integral Adeyeba, Titilope Adeola 09 May 2015 (has links) This thesis aims to simultaneously minimize function error and model complexity for data fusion via the Choquet integral (CI). The CI is a generator function, i.e., it is parametric and yields a wealth of aggregation operators based on the specifics of the underlying fuzzy measure. It is often the case that we desire to learn a fusion from data and the goal is to have the smallest possible sum of squared error between the trained model and a set of labels. However, we also desire to learn as “simple’’ of solutions as possible. Herein, L1-norm regularization of a lexicographically encoded capacity vector relative to the CI is explored. The impact of regularization is explored in terms of what capacities and aggregation operators it induces under different common and extreme scenarios. Synthetic experiments are provided in order to illustrate the propositions and concepts put forth. l1-norm regularization capacity learning minimum model complexity fuzzy integral Choquet integral
2	New Algorithms for Local and Global Fiber Tractography in Diffusion-Weighted Magnetic Resonance Imaging Schomburg, Helen 29 September 2017 (has links) No description available. 510 tractography fiber tracking diffusion MRI DTI Bayesian statistics white matter microstructure convex optimization l1-norm regularization Sobolev-norm regularization low-rank approximation medical image processing medical imaging orientation distribution function Mathematics (PPN61756535X)

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Insights and Characterization of l1-norm Based Sparsity Learning of a Lexicographically Encoded Capacity Vector for the Choquet Integral

New Algorithms for Local and Global Fiber Tractography in Diffusion-Weighted Magnetic Resonance Imaging