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Classifier competence is critical important for dynamic classifier selection. This study proposes a semi-supervised learning algorithm to learn the competence of classifiers under the proposed optimization framework based on graph. First it constructs a graph based on the training data and some unlabeled data. Then it iteratively learns the competence of classifiers. The learned competence not just...
This paper explores the classification problem based on parallel feature partitioning. This formulation leads to a new problem in computational geometry. While this new problem appears to be NP-complete, it is shown that the proposed graph theoretical platform makes it semi-tractable, allowing the use of conventional tools for its solution. Here, by conventional, we mean any exact or heuristic algorithm...
Abstract A new method of using Vis/NIR spectroscopy technique to identify the varieties of red wines was studied. Through comparing modeling performance built by different amounts of independent components, 20 independent components (ICs) extracted by independent components analysis (ICA) were employed as the inputs of the BP neural networks and were consider to be important parameter for calibration...
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