Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Kliegr, Tomáš"'
Autor:
Dvorackova, Lucie, Joachimiak, Marcin P., Cerny, Michal, Kubecova, Adriana, Sklenak, Vilem, Kliegr, Tomas
Best performing approaches for scholarly document quality prediction are based on embedding models, which do not allow direct explanation of classifiers as distinct words no longer correspond to the input features for model training. Although model-a
Externí odkaz:
http://arxiv.org/abs/2409.15912
Autor:
Adam, Daniel, Kliegr, Tomáš
This article presents a method for verifying RDF triples using LLMs, with an emphasis on providing traceable arguments. Because the LLMs cannot currently reliably identify the origin of the information used to construct the response to the user query
Externí odkaz:
http://arxiv.org/abs/2409.07507
Existing text representations such as embeddings and bag-of-words are not suitable for rule learning due to their high dimensionality and absent or questionable feature-level interpretability. This article explores whether large language models (LLMs
Externí odkaz:
http://arxiv.org/abs/2409.07132
Autor:
Lawrynowicz, Agnieszka, Galarraga, Luis, Alam, Mehwish, Jaulmes, Berenice, Zeman, Vaclav, Kliegr, Tomas
In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining methodologies, categorized into three groups: inductive logic programming
Externí odkaz:
http://arxiv.org/abs/2408.05773
Publikováno v:
American Behavioral Scientist 64.2 (2020): 145-175
The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a
Externí odkaz:
http://arxiv.org/abs/1911.03249
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Artificial Intelligence (2021): 103458
While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of
Externí odkaz:
http://arxiv.org/abs/1804.02969
Publikováno v:
Machine Learning 109(4):853-898, 2020
It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this
Externí odkaz:
http://arxiv.org/abs/1803.01316
Autor:
Kliegr, Tomas, Izquierdo, Ebroul
A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techni
Externí odkaz:
http://arxiv.org/abs/1711.10166
Publikováno v:
In Artificial Intelligence June 2021 295
