Mixtures of Boosted Classifiers for Frontal Face Detection

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Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

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Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of acade ...

Mixtures of Boosted Classifiers for Frontal Face Detection

Chattez avec Graph Search

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Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Classification of fall directions via wearable motion sensors