Stochastic gradient descent-based support vector machines training optimization on Big Data and HPC frameworks

被引：0

作者：

Abeykoon, Vibhatha ^{[1
]}

Fox, Geoffrey ^{[1
]}

Kim, Minje ^{[1
]}

Ekanayake, Saliya ^{[2
]}

Kamburugamuve, Supun ^{[1
]}

Govindarajan, Kannan ^{[1
]}

Wickramasinghe, Pulasthi ^{[1
]}

Perera, Niranda ^{[1
]}

Widanage, Chathura ^{[1
]}

Uyar, Ahmet ^{[1
]}

Gunduz, Gurhan ^{[1
]}

Akkas, Selahatin ^{[1
]}

机构：

[1] Intelligent Systems Engineering, Indiana University Bloomington, Bloomington,IN, United States

[2] Performance and Algorithm Research, Lawrence Berkeley National Laboratory, Berkeley,CA, United States

来源：

Concurrency and Computation: Practice and Experience | 2022年 / 34卷 / 08期

关键词：

D O I：

e6292

中图分类号：

学科分类号：

摘要：

Mean square error - Data flow analysis - Learning systems - Modeling languages - Big data - Hybrid systems - Learning algorithms - C++ (programming language) - Stochastic models - Stochastic systems - Benchmarking - Gradient methods - Optimization

引用

共 50 条

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