Interpretability of Bayesian Network Classifiers: OBDD Approximation and Polynomial Threshold Functions.

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Scholarly articles for Interpretability of Bayesian Network Classifiers: OBDD Approximation and Polynomial Threshold Functions.

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… of Bayesian Network Classifiers: OBDD Approximation …
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[PDF] Explaining Decisions in ML Models: a Parameterized Complexity Analysis

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Jul 23, 2024 · Challenging the notion of inherent opacity in ML models, our study includes Decision Trees (DTs), Decision. Sets (DSs), Decision Lists (DLs), and Ordered Binary ...

YOLO Object Detection Explained: Evolution, Algorithm, and Applications

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Jul 16, 2024 · It used a single convolutional neural network (CNN) to detect objects in images by dividing the image into a grid, making multiple predictions per grid cell, ...

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Gene Selection for Cancer Classification Using Support Vector ...

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Jul 23, 2024 · To assess the efficacy of each model in feature selection, we employ a Recursive Feature Elimination (RFE) approach [15] , involving the gradual removal of ...

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Webinar | Department of Electrical and Electronic Engineering - HKU EEE

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Jul 16, 2024 · The speaker will briefly describe these techniques. Furthermore, as the system complexity increases, neural networks are used to approximate the Q-values ( ...

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[PDF] Computer Aided Verification - ResearchGate

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Jul 24, 2024 · Specifically, our approach yields interpretable classifiers as binary decision trees. Figure 1b shows the abstract system and the automatically generated ...

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