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pro vyhledávání: '"Burgard, Jan Pablo"'
Random forests are among the most famous algorithms for solving classification problems, in particular for large-scale data sets. Considering a set of labeled points and several decision trees, the method takes the majority vote to classify a new giv
Externí odkaz:
http://arxiv.org/abs/2405.09832
When using machine learning for automated prediction, it is important to account for fairness in the prediction. Fairness in machine learning aims to ensure that biases in the data and model inaccuracies do not lead to discriminatory decisions. E.g.,
Externí odkaz:
http://arxiv.org/abs/2405.09273
To ensure unbiased and ethical automated predictions, fairness must be a core principle in machine learning applications. Fairness in machine learning aims to mitigate biases present in the training data and model imperfections that could lead to dis
Externí odkaz:
http://arxiv.org/abs/2405.06433
Among the most famous algorithms for solving classification problems are support vector machines (SVMs), which find a separating hyperplane for a set of labeled data points. In some applications, however, labels are only available for a subset of poi
Externí odkaz:
http://arxiv.org/abs/2303.12532
Variance parameter estimation in linear mixed models is a challenge for many classical nonlinear optimization algorithms due to the positive-definiteness constraint of the random effects covariance matrix. We take a completely novel view on parameter
Externí odkaz:
http://arxiv.org/abs/2212.09081
In the last year many public health decisions were based on real-time monitoring the spread of the ongoing COVID-19 pandemic. For this one often considers the reproduction number which measures the amount of secondary cases produced by a single infec
Externí odkaz:
http://arxiv.org/abs/2108.13842
Autor:
Burgard, Jan Pablo1 (AUTHOR), Moreira Costa, Carina2 (AUTHOR), Schmidt, Martin2 (AUTHOR) martin.schmidt@uni-trier.de
Publikováno v:
Annals of Operations Research. Aug2024, Vol. 339 Issue 3, p1525-1568. 44p.
Publikováno v:
Stat Comput 32, 8 (2022)
Gaussian Mixture Models are a powerful tool in Data Science and Statistics that are mainly used for clustering and density approximation. The task of estimating the model parameters is in practice often solved by the Expectation Maximization (EM) alg
Externí odkaz:
http://arxiv.org/abs/2104.14957
Publikováno v:
In Econometrics and Statistics September 2024
Publikováno v:
TOP; Oct2024, Vol. 32 Issue 3, p391-428, 38p