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Skip to Search Results- 3Learning theory
- 2Directionality
- 2Image processing
- 2Machine learning
- 2Tensor product
- 1Constrained Markov Decision Process
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Spring 2017
Optimizing an objective function over convex sets is a key problem in many different machine learning models. One of the various kinds of well studied objective functions is the convex function, where any local minimum must be the global mini- mum over the domain. To find the optimal point that...
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Fall 2015
This thesis concentrates on the construction of directional tensor product complex tight framelets. It uses a complex tight framelet filter bank in one dimension and the tensor product of the one-dimensional filter bank to obtain high-dimensional filter bank. It has a number of advantages over...
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Fall 2013
It is well known that most information of an image is contained in its edges. Therefore capturing edges in an image is of fundamental importance in image processing. Tensor product real-valued wavelets only capture edges along the horizontal and vertical direc- tions. Hence they are only...
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Fall 2017
On the one hand, theoretical analyses of machine learning algorithms are typically performed based on various probabilistic assumptions about the data. While these probabilistic assumptions are important in the analyses, it is debatable whether such assumptions actually hold in practice. Another...
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Fall 2023
Many real-world tasks in fields such as robotics and control can be formulated as constrained Markov decision processes (CMDPs). In CMDPs, the objective is usually to optimize the return while ensuring some constraints being satisfied at the same time. The primal-dual approach is a common...