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- W575297476 abstract "This master thesis explores a dual-tree framework with underlying kd-tree space partitioning data structure as applied to a particular class of machine learning problems that are collectively referred to as generalized n-body problems. We propose a novel algorithm based on the dual-tree framework to accelerate the task of discovering characterizing boundary points (CBP) – a set of data points defined by geometry rules and representing an optimal robust interclass boundary. Designed with support for both approximate and exact computations, experimental results confirm superior runtime properties of the algorithm compared to a state-of-the-art solution. Furthermore, we propose an improvement of the Boosted Geometry-Based Ensembles algorithm that constructs a CBP-based strong classifier. Owning to our modification of the original learner, the scalability of the algorithm is improved to be able to operate on the datasets of higher size and dimensions while improving speed and maintaining high accuracy of classification." @default.
- W575297476 created "2016-06-24" @default.
- W575297476 creator A5041211062 @default.
- W575297476 date "2015-04-30" @default.
- W575297476 modified "2023-09-27" @default.
- W575297476 title "Machine learning approximation techniques using dual trees" @default.
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