Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to mak...
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Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model's prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.
An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is developed and presented. This joint physics/statistics based technique generates images that ...
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ISBN:
(纸本)0819449555
An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is developed and presented. This joint physics/statistics based technique generates images that have many of the "blob-like" and "spiky" clutter characteristics of UWB radar data in forested regions while avoiding the intensive computations required for the implementation of low-frequency numerical electromagnetic simulation techniques. Approaches towards developing "self-training" algorithms for UWB radar target detection are investigated using the results of this simulation process. These adaptive approaches employ some form of modified singular value decomposition (SVD) algorithm where small blocks of data in the neighborhood of a sliding test window are processed in real-time in an effort to estimate localized clutter characteristics. These real-time local clutter models are then used to cancel clutter in the sliding test window. Comparative results from three SVD-based approaches to adaptive and "self-trained" target detection algorithms are reported. These approaches are denoted as "Energy-Normalized SVD", "Condition-Statistic SVD", and "Terrain-Filtered SVD". The results indicate that the "Terrain-Filtered SVD" approach, where a pre-filter is applied in an effort to eliminate severe clutter discretes that adversely effect performance, appears promising for the purposes of developing "self-traiming" algorithms for applications that may require localized "on-the-fly" training due to a lack of accurate off-line training data.
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