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Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge.

, , , , , , , , , , , , , , , , and . IEEE Trans. Med. Imaging, 34 (9): 1890-1900 (2015)

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Machine Learning in Systems Biology. Proc. 3rd International Workshop, , and . (2009)Gradient tree boosting with random output projections for multi-label classification and multi-output regression., , and . CoRR, (2019)Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging., , , , , , and . Neural Connectomics, volume 46 of JMLR Workshop and Conference Proceedings, page 23-34. JMLR.org, (2014)Tree-Based Batch Mode Reinforcement Learning., , and . J. Mach. Learn. Res., (2005)Learning to rank with extremely randomized trees., and . Yahoo! Learning to Rank Challenge, volume 14 of JMLR Proceedings, page 49-61. JMLR.org, (2011)Towards generic image classification using tree-based learning: An extensive empirical study., , and . Pattern Recognit. Lett., (2016)Statistical interpretation of machine learning-based feature importance scores for biomarker discovery., , , and . Bioinform., 28 (13): 1766-1774 (2012)Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities., , , , and . Neurocomputing, 70 (16-18): 2668-2678 (2007)Context-dependent feature analysis with random forests., , , , and . UAI, AUAI Press, (2016)Comparator selection for RPC with many labels., , and . ECAI, volume 242 of Frontiers in Artificial Intelligence and Applications, page 408-413. IOS Press, (2012)