نتائج البحث

Generation of virtual patient data for in-silico cardiomyopathies drug development using tree ensembles: a comparative study

• Authors : Dimitrios I. Fotiadis; Grigorios I. Grigoriadis; Iacopo Olivotto

Subjects: 0301 basic medicine; Multivariate statistics; Computer science

• Source: EMBC

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Efficient traversal of decision tree ensembles with FPGAs

• Authors : Veronica Gil-Costa; Fernando Loor; Salvatore Trani ...

Subjects: System on Chip; Computer Networks and Communications; Computer science

• Source: Journal of parallel and distributed computing155 (2021): 38–49. doi:10.1016/j.jpdc.2021.04.008info:cnr-pdr/source/autori:Molina R.; Loor F.; Gil-Costa V.; Nardini F.M.; Perego R.; Trani

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Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)

• Authors : Tien Foo Sing; Shi Ming Yu; Jesse Jingye Yang

Subjects: Economics and Econometrics; Boosting (machine learning); Housing markets

• Source: The Journal of Real Estate Finance and Economics

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Bayesian Survival Tree Ensembles with Submodel Shrinkage

• Authors : Yinpu Li; Antonio R. Linero; Piyali Basak

Subjects: Statistics and Probability; Statistics::Theory; business.industry

• Source: Bayesian Analysis. 17

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Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study

• Authors : Amir Ahmad; Sharaf Jameel Malebary; Entisar S. Alkayal

Subjects: 2019-20 coronavirus outbreak; Multidisciplinary; General Computer Science

• Source: Complexity, Vol 2021 (2021)

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Relational tree ensembles and feature rankings

• Authors : Matej Petković; Michelangelo Ceci; Gianvito Pio

Subjects: Information Systems and Management; Artificial Intelligence; Software

• Source: Knowledge-Based Systems. 251:109254

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Interpreting tree ensembles with inTrees

• Authors : Houtao Deng

Subjects: FOS: Computer and information sciences; 0301 basic medicine; Computer science

• Source: International Journal of Data Science and Analytics. 7:277-287

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TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity

• Authors : Sebastian Gombert; Sabine Bartsch

Subjects: business.industry; Computer science; Bigram

• Source: SemEval@ACL/IJCNLP

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Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity

• Authors : Antonio R. Linero; Yun Yang

Subjects: Statistics and Probability; business.industry; Posterior probability

• Source: Journal of the Royal Statistical Society Series B: Statistical Methodology. 80:1087-1110

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Energy Modeling of Hoeffding Tree Ensembles

• Authors : Albert Bifet; Niklas Lavesson; Eva García-Martín

Subjects: Energy utilization; Mathematical optimization; Computer science

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