Essays about: "Regularization methods"
Showing result 1 - 5 of 64 essays containing the words Regularization methods.
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1. Regularization Methods and High Dimensional Data: A Comparative Study Based on Frequentist and Bayesian Methods
University essay from Lunds universitet/Statistiska institutionenAbstract : As the amount of high dimensional data becomes increasingly accessible and common, the need for reliable methods to combat problems such as overfitting and multicollinearity increases. Models need to be able to manage large data sets where predictor variables often outnumber the amount of observations. READ MORE
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2. Fracture simulation with a hyperelastic phase field model
University essay from KTH/HållfasthetsläraAbstract : The phase field method is a versatile tool to study crack initiation and propagation in systems with complex geometries. Based on a variational formulation of the equilibrium equations, the sharp crack topology is regularized by a crack with diffusive edges and the damage is described by a continuous phase field variable. READ MORE
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3. Comparing dropout regularization algorithms for convolutional neural networks identifying malignant cells for diagnosis of leukemia
University essay from Uppsala universitet/Statistiska institutionenAbstract : Fast and high quality classifications of cells inflicted with malignant mutations is essential for diagnosing patients with different forms of leukemia, to quickly be able give patients the crucial care they need. Convolutional neural networks (CNNs) can be trained and used for this purpose. READ MORE
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4. Deep Learning-based Regularizers for Cone Beam Computed Tomography Reconstruction
University essay from KTH/Matematisk statistikAbstract : Cone Beam Computed Tomography is a technology to visualize the 3D interior anatomy of a patient. It is important for image-guided radiation therapy in cancer treatment. During a scan, iterative methods are often used for the image reconstruction step. READ MORE
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5. Explainable Machine Learning in Cardiovascular Diagnostics
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The major challenges in implementing machine learning models in medical applications stemfrom ethical and accountability concerns, which arise from the lack of insight and understandingof the models' inner workings and reasoning. This opaqueness has resulted in the emergenceof a new subfield of machine learning called Explainability, which aims to develop and deploymethods to gain insight into how input data is weighted and propagated through a machinelearning algorithm. READ MORE
