Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field... Show more Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop computer programs capable of building models (algorithms). Many of such algorithms exist, and these often consist of various options that subtly influence performance (parameters). Furthermore, there is mathematical proof that there exists no single algorithm that works well on every dataset. This complicates the task of selecting the right algorithm for a given task. The field of meta-learning aims to resolve these problems. The purpose is to determine what kind of algorithms work well on which datasets. In order to do so, we developed OpenML. This is an online database on which researches can share experimental results amongst each other, potentially scaling up the size of meta-learning studies. Having earlier experimental results freely accessible and reusable for others, it is no longer required to conduct time expensive experiments. Rather, researchers can answer such experimental questions by a simple database look-up. This thesis addresses how OpenML can be used to answer fundamental meta-learning questions. Show less
In X-ray tomography, a three-dimensional image of the interior of an object is computed from multiple X-ray images, acquired over a range of angles. Two types of methods are commonly used to... Show moreIn X-ray tomography, a three-dimensional image of the interior of an object is computed from multiple X-ray images, acquired over a range of angles. Two types of methods are commonly used to compute such an image: analytical methods and iterative methods. Analytical methods are computationally efficient, but in many applications, they produce reconstructions that are not accurate enough for further analysis. More accurate reconstructions can be obtained by using (regularized) iterative methods, but these can have computational costs that are too high to be used in practice. In this thesis, new reconstruction methods are developed that combine the analytical and algebraic approaches, resulting in methods that are as computationally efficient as analytical methods, but with a reconstruction accuracy of iterative methods. Analytical methods allow for changing their filter without increasing the needed computation time. We use this freedom in filter choice to develop new filter-based reconstruction methods, which are based on the analytical FBP method with specific filters. The filters can be defined and computed in different ways, and can depend on the acquisition geometry, the scanned object, and/or a separate pre-computing step. Several filter-based methods are introduced in this thesis and reconstruction results are compared with other popular methods. Show less
