Asynch-SGBDT: Train Stochastic Gradient Boosting Decision Trees in an Asynchronous Parallel Manner-Reference-Cited by-同舟云学术

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Asynch-SGBDT: Train Stochastic Gradient Boosting Decision Trees in an Asynchronous Parallel Manner

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
language:
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Author:

Daning Cheng¹,Shigang Li²,Yunquan Zhang³

Affiliation:

1. Tsinghua University,Dept. of Computer Science and Technology,Beijing,China

2. Beijing University of Posts and Telecommunications,School of Computer Science,Beijing,China

3. Chinese Academy of Sciences,Institute of Computing Technology,Beijing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10177277/10177383/10177412.pdf?arnumber=10177412

Reference30 articles.

1. DimBoost

2. Lightgbm: A highly efficient gradient boosting decision tree;ke,0

3. Introducing the webb spam corpus: Using email spam to identify web spam automatically;webb;CEAS 2006 Third Conference on Email and Anti-Spam,2006

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