A Bayesian Multiclass Approach for Estimation of Airbag Trajectories from Mobile Health Apps


A Bayesian Multiclass Approach for Estimation of Airbag Trajectories from Mobile Health Apps – We present a machine learning method for automatically categorizing airbag videos using image segmentation. The method uses a visual representation and a hierarchical clustering to find the category-specific airbag segmentation clusters. We first provide a baseline for such a machine learning algorithm. After that, we provide three preliminary experiments to compare the proposed method with state of the art image segmentation methods. We observe that the proposed method performs significantly faster than state-of-the-art methods. Finally, we compare the proposed method with the state of the art methods in terms of both training and evaluation time on video datasets.

We present a new class of Bayesian networks based on the Markov chains to learn high-dimensional embeddings of inputs and outputs. The Markov chain models the inputs and outputs, taking the inputs and outputs, and the input and output dimensions as their hidden units. Experiments show that the proposed learning method performs significantly better than a generic method in terms of both accuracy and training time.

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A Bayesian Multiclass Approach for Estimation of Airbag Trajectories from Mobile Health Apps

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    Euclidean Metric Learning with Exponential FamiliesWe present a new class of Bayesian networks based on the Markov chains to learn high-dimensional embeddings of inputs and outputs. The Markov chain models the inputs and outputs, taking the inputs and outputs, and the input and output dimensions as their hidden units. Experiments show that the proposed learning method performs significantly better than a generic method in terms of both accuracy and training time.


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