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High-Performance Ensembles of Online Sequential Extreme Learning Machine for Regression and Time Series Forecasting

فرق عالية الأداء لآلة التعلم المتطرف المتسلسلة عبر الإنترنت للتنبؤ بالانحدار والسلاسل الزمنية
Authors: Luis Fernando L. Grim; André Leon S. Gradvohl;

High-Performance Ensembles of Online Sequential Extreme Learning Machine for Regression and Time Series Forecasting

Abstract

These slides were presented at the 1st High Performance Machine Learning (HPML) workshop, held in conjunction with the 30th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) 2018. The paper which this presentation is based on is entitled "High-Performance Ensembles of Online Sequential Extreme Learning Machine for Regression and Time Series Forecasting" and is available with the following DOI: 10.1109/SBAC-PAD.2018.00068

{"references": ["Grim, L. F. L. and Gradvohl, A. L. S. High-Performance Ensembles of Online Sequential Extreme Learning Machine for Regression and Time Series Forecasting. DOI: 10.1109/SBAC-PAD.2018.00068"]}

Keywords

Parallel computing, Instruction set, Artificial neural network, Artificial intelligence, Execution time, Time series, Extreme learning machine, Ensemble Classifiers, High Performance Computing, Set (abstract data type), Speedup, Artificial Intelligence, Machine learning, Swarm Intelligence Optimization Algorithms, Theory and Applications of Extreme Learning Machines, Adaptation to Concept Drift in Data Streams, Ensemble Learning, Multi-core processor, Extreme Learning Machines, Computer science, Regression, Process (computing), Programming language, Online Sequential Learning, Online Learning, Implementation, Computer Science, Physical Sciences, Extreme Learning Machine, Forecasting

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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