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pro vyhledávání: '"Yang, Lincen"'
Autor:
Yang, Lincen, van Leeuwen, Matthijs
Conditional density estimation (CDE) goes beyond regression by modeling the full conditional distribution, providing a richer understanding of the data than just the conditional mean in regression. This makes CDE particularly useful in critical appli
Externí odkaz:
http://arxiv.org/abs/2410.11449
Autor:
Yang, Lincen, van Leeuwen, Matthijs
Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while lea
Externí odkaz:
http://arxiv.org/abs/2206.08804
Estimating conditional mutual information (CMI) is an essential yet challenging step in many machine learning and data mining tasks. Estimating CMI from data that contains both discrete and continuous variables, or even discrete-continuous mixture va
Externí odkaz:
http://arxiv.org/abs/2101.05009
Unsupervised discretization is a crucial step in many knowledge discovery tasks. The state-of-the-art method for one-dimensional data infers locally adaptive histograms using the minimum description length (MDL) principle, but the multi-dimensional c
Externí odkaz:
http://arxiv.org/abs/2006.01893
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