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of 96
pro vyhledávání: '"Duivesteijn, Wouter"'
Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting, ensembles of local decision rules can gain good predictive performan
Externí odkaz:
http://arxiv.org/abs/2109.10432
We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been analyzed empirically, but it has never before been mathematically der
Externí odkaz:
http://arxiv.org/abs/2001.01987
Autor:
Hess, Sibylle, Duivesteijn, Wouter
Most convex and nonconvex clustering algorithms come with one crucial parameter: the $k$ in $k$-means. To this day, there is not one generally accepted way to accurately determine this parameter. Popular methods are simple yet theoretically unfounded
Externí odkaz:
http://arxiv.org/abs/1907.02343
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms h
Externí odkaz:
http://arxiv.org/abs/1907.00680
Publikováno v:
Data Mining and Knowledge Discovery, (2021), 1-26
Learning causal effects from observational data greatly benefits a variety of domains such as health care, education and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist the clinic plan and improv
Externí odkaz:
http://arxiv.org/abs/1904.13335
Finding regions for which there is higher controversy among different classifiers is insightful with regards to the domain and our models. Such evaluation can falsify assumptions, assert some, or also, bring to the attention unknown phenomena. The pr
Externí odkaz:
http://arxiv.org/abs/1808.07243
Autor:
Lijffijt, Jefrey, Kang, Bo, Duivesteijn, Wouter, Puolamäki, Kai, Oikarinen, Emilia, De Bie, Tijl
Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the fact that there are exponentially many variable combinations to potentially consider, and there are infinitely many if we co
Externí odkaz:
http://arxiv.org/abs/1710.04521
Akademický článek
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Autor:
Duivesteijn, Wouter
Publikováno v:
Journal of the American Statistical Association, 2016 Sep 01. 111(515), 1370-1371.
Externí odkaz:
https://www.jstor.org/stable/45028380