Parallel and Distributed Machine Learning Algorithms for Scalable Big Data AnalyticsThis editorial is for the Special Issue of the journal Future Generation Computing Systems, consisting of the selected papers o
Parallel Machine Learning Algorithms 2023, Mesopotamian Journal of Big Data Straggler-Aware In-Network Aggregation for Accelerating Distributed Deep Learning 2023, IEEE Transactions on Services Computing FSP: Towards Flexible Synchronous Parallel Frameworks for Distributed Machine Learning 2023, IEEE Transactions...
As an important method in the field of machine learning, ensemble learning has been shown to provide significant improvement to the generalization ability of algorithms as early as in the classification and clustering tasks5,6. Introducing the idea of ensemble into anomaly detection reduces the ...
Therefore, a large number of research works have been focused on Android malware detection by using various machine learning algorithms. For example, Drebin [3] extracted several kinds of features, such as API calls and permissions, and used the support vector machine (SVM) algorithm to detect ...
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high-performanceparallel-computingevolutionary-algorithmsgaesmoeaddegeatpynsgarvea UpdatedJan 17, 2025 Python mfem/mfem Star1.9k Code Issues Pull requests Discussions Lightweight, general, scalable C++ library for finite element methods hpcparallel-computingscientific-computinghigh-performance-computingamrfemfinit...
会议日期: 2025-10-30 会议地点: Zhengzhou, China 届数: 25 CCF:cCORE:bQUALIS:b3浏览:89854关注:202参加:73 征稿 ICA3PP 2025 is the 25th in this series of conferences started in 1995 that are devoted to algorithms and architectures for parallel processing. ICA3PP is a famous event worldwide th...
Cosnard, M., Bungartz, HJ., Gallopoulos, E., Saad, Y. (2006). Topic 10: Parallel Numerical Algorithms. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds) Euro-Par 2006 Parallel Processing. Euro-Par 2006. Lecture Notes in Computer Science, vol 4128. Springer, Berlin, Heidelberg....
Parallel and Distributed Combinatorial & Numerical Methods, Scheduling Algorithms for Parallel and Distributed Applications and Platforms, Algorithmic Innovations for Parallel and Distributed Machine Learning, Post-Moore parallel algorithms. Performance: Performance: Performance Modeling of Parallel or Distributed ...
Stochastic gradient descent (SGD) is a popular stochastic optimization method in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel SGD (Zinkevich, 2010), often require all nodes to have the same performance or to consume equal quantities of data. However, these requirements...