Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 79 for “"Regularisation"”.
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A Multiplicative Regularisation for Inverse Problems
This thesis considers self-adaptive regularisation methods, focusing particularly on new, multiplicative methods, in which the cost functional is constructed as a product of two terms, rather than the more usual sum of a fidelity term and a regularisation term. By re-formulating the multiplicative …
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Regularisation methods for imaging from electrical measurements
… problem, the stable solution of which requires regularisation methods. Most common regularisation methods impose that the reconstructed image should be smooth. Such methods confer stability to the reconstruction process, but limit the capability of describing sharp variations in the sought …
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Minimum description length, regularisation and multi-modal data
… the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics …
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Novel higher order regularisation methods for image reconstruction
… are formulated in the context of Tikhonov regularisation. We focus on regularisation techniques in which the regulariser incorporates second order derivatives or a sophisticated combination of first and second order derivatives. The introduction of higher order derivatives in the …
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Machine Learning in Inverse Problems - Learning Regularisation Functionals and Operator Corrections
… approach to inverse problems is variational regularisation, combining knowledge and modelling of the imaging modality at hand with a regularisation functional that incorporates prior knowledge about solutions to the inverse problem. With the success of deep neural networks in many imaging …
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Mathematical Imaging Tools in Cancer Research - From Mitosis Analysis to Sparse Regularisation
… investigated further in the framework of image regularisation in the general context of imaging inverse problems, in which circular objects should be enhanced, (ii) exploiting sparsity of first-order derivatives in combination with the linear circular Hough transform operation. Furthermore, …
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First-order gradient regularisation methods for image restoration: reconstruction of tomographic images with thin structures and denoising piecewise affine images
… gradient regularisers: Total Variation (TV) regularisation in image and data space for reconstruction of thin structures from PET data and regularisers given by an infimal-convolution of TV and $L^p$ seminorms for denoising images with piecewise affine structures. In the first part of this …
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Legalising the illegal: an assessment of the Dispensation of Zimbabweans Project (DZP) and Zimbabwe Special Dispensation Permit (ZSP) regularisation projects
… undocumented migrants were documented during the regularisation processes. The DZP and ZSP projects complemented South Africa's highly restrictive approach to migration management and jealous safeguarding of access to permanent residence and citizenship. The regularisation projects also enabled …
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Structured Deep Neural Networks for Speech Recognition
… network structures and parameters causes better regularisation and adaptation on DNN models challenging. In regularisation, parameters have to be regularised universally and indiscriminately. For instance, the widely used L2 regularisation encourages all parameters to be zeros. In adaptation, it …
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Finite system size effects on the effective coupling in scalar quantum field theory
… amplitude calculation using dimensional regularisation, and then introduce and employ denominator regularisation. We then perform the non-trivial calculation of the NLO 2 → 2 scattering amplitude in our compactified spacetime. This requires the derivation of an new analytic continuation …
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New PDE models for imaging problems and applications
… In their general form these models combine a regularisation and a data fitting term, balancing one against the other appropriately. Total variation (TV) regularisation is often used due to its edgepreserving and smoothing properties. In this thesis, we focus on the design of TV-based models …
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The illusion of community participation: experience in the irregular settlements of Bogotá
… practices became less effective to push the regularisation process. City programmes toward irregular settlements became more holistic and benefited from better coordination between the different public entities. As a result, the inhabitants became more discriminating in identifying the most …
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Learning to Coordinate Efficiently through Multiagent Soft Q-Learning in the presence of Game-Theoretic Pathologies
… modelling approaches with mutual information regularisation. We find that while the former (the use of efficient samplers) is not as helpful as one would wish, the latter (opponent modelling with mutual information regularisation) offers new insights into the required mechanism to solve our …
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The Structure of Baryon Resonances
… Hamiltonian has been made finite by finite-range regularisation, and by considering the range of regularisation parameters which allow the Hamiltonian to describe experimental scattering data, insight is gained into the degree of model-dependence in the infinite-volume and finite-volume …
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Tomographic Reconstruction of 2-D Atmospheric Trace Gas Distributions from Active DOAS Measurements
… can tremendously improve the reconstruction. Regularisation plays a minor role. Proposals for retrieving peak distributions by combining different parametrisations are again examined systematically showing that their usefulness heavily depends on the features one is most interested in. …
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Deep Learning Methods for Muscle Analysis From Magnetic Resonance Images
… improve its performance through a novel hybrid regularisation mechanism. Its practicality is further demonstrated through its state-of-the-art performance on musculoskeletal data. We also further evaluate its performance on two other datasets with varying anisotropy to showcase its …
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Structure-preserving machine learning for inverse problems
… are generally assumed to be known). Variational regularisation is a well-established approach that can be used to approximately solve inverse problems such as Problem (1). In this approach an image is reconstructed from measurements y by solving a minimisation problem such as uˆ = argmin …
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Estimating time delays between irregularly sampled time series
… is evolved to perform model selection, regularisation and time delay estimation globally and simultaneously. Experimental results show that this approach is one of the most accurate methods for gaps (missing data) and distinct noise levels. Results on artificial and real data are shown.
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Flexible risk-based portfolio optimisation
… include regime switching, quantile regression, regularisation and subset resampling. In the South African experiment, risk-based portfolios materially outperformed the market weight portfolio out-of-sample using a Sharpe ratio measure. Additionally, the global minimum variance portfolio …
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