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Title: Bayesian and Classical Audit Samplingĭescription: Implements the audit sampling workflow as discussed in Derks et al. Package jfa updated to version 0.5.4 with previous version 0.5.3 dated Title: Access the Global Plant Phenology Data Portalĭescription: Search plant phenology data aggregated from several sources and available on the Global Plant Phenology Data Portal.ĭiff between rppo versions 1.0.1 dated and 1.0.2 dated Package rppo updated to version 1.0.2 with previous version 1.0.1 dated More information about shattering at CRAN (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".ĭiff between shattering versions 1.0.5 dated and 1.0.6 dated (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" de Mello, R.F., Ponti, M.A. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. Title: Estimate the Shattering Coefficient for a Particular Datasetĭescription: The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F.

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Package shattering updated to version 1.0.6 with previous version 1.0.5 dated










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