Results 281 to 290 of about 138,756 (354)

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach. [PDF]

Biom J
Castro-Prado F, González-Manteiga W, Costas J, Facal F, Edelmann D.   +4 more
europepmc   +1 more source

A tendency coefficient-driven Pythagorean fuzzy distance approach for selection problems in higher education and medical waste management. [PDF]

Sci Rep
Ejegwa PA, Anum MT, Kausar N, Nurgaliyeva A, Salman MA.   +4 more
europepmc   +1 more source

Intelligent strength training for football players using resnext optimized by upgraded chimp optimization algorithm. [PDF]

Sci Rep
Zhang W, Zhang Y.
europepmc   +1 more source

Integrating Medication Leaflets Utilizing FHIR and an LLM-Based Question-Answer Pipeline in a Mobile Application. [PDF]

J Med Syst
Kirchsteiger K, Heiler L, Bödenler M, Hanke S.   +3 more
europepmc   +1 more source

Cardinality and Identity

Journal of Philosophical Logic, 2007
P.T. Geach has maintained (see, e.g., Geach (1967/1968)) that identity (as well as dissimilarity) is always relative to a general term. According to him, the notion of absolute identity has to be abandoned and replaced by a multiplicity of relative identity relations for which Leibniz’s Law – which says that if two objects are identical they have the ...
CARRARA, MASSIMILIANO, SACCHI, ELISABETTA   +1 more
core   +4 more sources

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Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation

Proceedings of the VLDB Endowment, 2021
Cardinality estimation (CardEst) plays a significant role in generating high-quality query plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced CardEst methods (especially ML-based) have been proposed with ...
Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Tan, K. Zeng, G. Cong, Yanzhao Qin, A. Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui   +13 more
semanticscholar   +1 more source

Learned Cardinality Estimation: An In-depth Study

SIGMOD Conference, 2022
Learned cardinality estimation (CE) has recently gained significant attention for replacing long-studied traditional CE with machine learning, especially for deep learning.
Kyoungmin Kim, Jisung Jung, In Seo, Wook-Shin Han, Kangwook Choi, Jaehyok Chong   +5 more
semanticscholar   +1 more source

Flow-Loss: Learning Cardinality Estimates That Matter

Proceedings of the VLDB Endowment, 2021
Recently there has been significant interest in using machine learning to improve the accuracy of cardinality estimation. This work has focused on improving average estimation error, but not all estimates matter equally for downstream tasks like query ...
Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, N. Tatbul, Tim Kraska, Mohammad Alizadeh   +6 more
semanticscholar   +1 more source