The first and second ML algorithms are tested. Either the first or second ML algorithm is selected based at least in part on results of the testing. The selected ML algorithm is retained as a trained ML algorithm for predicting one or more of the plurality of features based on one or ...
Computing High-performance Scientific Computing Parallel and Distributed Algorithms Resource Management and Scheduling Multimedia in Parallel Computing Parallel Computing in Bioinformatics Parallel Machine Learning Algorithms Industrial Applications Dependability Issues in Computer Networks and Communications Dependability ...
Examples − Parallel quick sort, sparse matrix factorization, and parallel algorithms derived via divide-and-conquer approach.Here, problems are divided into atomic tasks and implemented as a graph. Each task is an independent unit of job that has dependencies on one or more antecedent task. ...
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...
A unified API standardizes many of today’s tools, frameworks, and algorithms, streamlining the distributed ML experience. This enables developers to quickly compose disparate ML frameworks for use cases that require more than one framework, such as web-supervised learning, search engine ...
high-performance parallel-computing evolutionary-algorithms ga es moead de geatpy nsga rvea Updated Jan 17, 2025 Python mfem / mfem Star 1.9k Code Issues Pull requests Discussions Lightweight, general, scalable C++ library for finite element methods hpc parallel-computing scientific-computing high...
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. microsoft python machine-learning data-mining r parallel distributed kaggle gbdt gbm lightgbm gbr...
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 ...
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 ...
Many machine learning algorithms have been designed based on MapReduce, but there are only a few works related to parallel extreme learning machine (ELM) which is a fast and accurate learning algorithm. Online sequential extreme learning machine (OS-ELM) is one of improved ELM algorithms to ...