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Full-Text Articles in Physical Sciences and Mathematics
Data Visualization And Classification Of Artificially Created Images, Dmytro Dovhalets
Data Visualization And Classification Of Artificially Created Images, Dmytro Dovhalets
All Master's Theses
Visualization of multidimensional data is a long-standing challenge in machine learning and knowledge discovery. A problem arises as soon as 4-dimensions are introduced since we live in a 3-dimensional world. There are methods out there which can visualize multidimensional data, but loss of information and clutter are still a problem. General Line Coordinates (GLC) can losslessly project n-dimensional data in 2- dimensions. A new method is introduced based on GLC called GLC-L. This new method can do interactive visualization, dimension reduction, and supervised learning. One of the applications of GLC-L is transformation of vector data into image data. This novel …
Decreasing Occlusion And Increasing Explanation In Interactive Visual Knowledge Discovery, Abdulrahman Ahmed Gharawi
Decreasing Occlusion And Increasing Explanation In Interactive Visual Knowledge Discovery, Abdulrahman Ahmed Gharawi
All Master's Theses
Lack of explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This thesis proposes a hybrid method that combines visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D in a set of Shifted Paired Coordinates (SPC). SPC for n-D data consists of n/2 pairs of Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies …
Spike-Based Classification Of Uci Datasets With Multi-Layer Resume-Like Tempotron, Sami Abdul-Wahid
Spike-Based Classification Of Uci Datasets With Multi-Layer Resume-Like Tempotron, Sami Abdul-Wahid
All Master's Theses
Spiking neurons are a class of neuron models that represent information in timed sequences called ``spikes.'' Though predominantly used in neuro-scientific investigations, spiking neural networks (SNN) can be applied to machine learning problems such as classification and regression. SNN are computationally more powerful per neuron than traditional neural networks. Though training time is slow on general purpose computers, spike-based hardware implementations are faster and have shown capability for ultra-low power consumption. Additionally, various SNN training algorithms have achieved comparable performance with the State of the Art on the Fisher Iris dataset. Our main contribution is a software implementation of the …