Suchergebnisse - parallel OS-ELM algorithm

Spark-based Parallel OS-ELM Algorithm Application for Short-term Load Forecasting for Massive User Data von Li, Yuancheng, Yang, Rongyan, Guo, Panpan

“… Aiming at this problem, a parallel OS-ELM short-term load forecasting model based on Spark is proposed in this article …”
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Parallel online sequential extreme learning machine based on MapReduce von Wang, Botao, Huang, Shan, Qiu, Junhao, Liu, Yu, Wang, Guoren

“… Many machine learning algorithms have been designed based on MapReduce, but there are only a few works related to parallel extreme learning machine (ELM …”
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System-on-a-Chip (SoC)-Based Hardware Acceleration for an Online Sequential Extreme Learning Machine (OS-ELM) von Safaei, Amin, Wu, Q. M. Jonathan, Akilan, Thangarajah, Yang, Yimin

“… ) and online sequential ELMs (OS-ELMs) are well known for their computational efficiency and performance when processing large datasets …”
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An OS-ELM based distributed ensemble classification framework in P2P networks von Sun, Yongjiao, Yuan, Ye, Wang, Guoren

“… In this paper, we propose an OS-ELM based ensemble classification framework for distributed classification in a hierarchical P2P network …”
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Bayes-OS-ELM :An Novel Ensemble Method For Classification Application von Zhu, Qingyu, Bai, Rui, Li, Mengting, Chen, Shaowei, Wen, Pengfei

“… In this paper, a ensemble method based on OS-ELM and Naive Bayes(Bayes-OS-ELM) has been developed. The ensemble model establishes parallel sub-classifiers with OS-ELM …”
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An FPGA-Based Accelerator for Graph Embedding using Sequential Training Algorithm von Sunaga, Kazuki, Sugiura, Keisuke, Matsutani, Hiroki

“… node2vec is a well-known algorithm to obtain such a graph embedding by sampling neighboring nodes on a given graph with a random walk technique …”
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An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning von Watanabe, Hirohisa, Tsukada, Mineto, Matsutani, Hiroki

“… It exploits a recently proposed neural-network based on-device learning approach that does not rely on the backpropagation method but uses OS-ELM …”
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Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes von Kouzehkalani Sales, Ali, Gul, Enes, Safari, Mir Jafar Sadegh

“… In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM …”
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Parallel ensemble of online sequential extreme learning machine based on MapReduce von Huang, Shan, Wang, Botao, Qiu, Junhao, Yao, Jitao, Wang, Guoren, Yu, Ge

“… As one of the ELM variants, online sequential extreme learning machine (OS-ELM) provides a method to analyze incremental data …”
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GPU‐accelerated and mixed norm regularized online extreme learning machine von Polat, Önder, Kayhan, Sema Koç

“… An online version of ELM called online sequential extreme learning machine (OS‐ELM) has also been proposed for the sequential training …”
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Living Tree Moisture Content Detection Method Based on Intelligent UHF RFID Sensors and OS-PELM von Wu, Yin, Zhang, Chengwu, Liu, Wenbo

“… Moisture content (MC) detection plays a vital role in the monitoring and management of living trees. Its measurement accuracy is of great significance to the …”
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PR-ELM: Parallel regularized extreme learning machine based on cluster von Wang, Yueqing, Dou, Yong, Liu, Xinwang, Lei, Yuanwu

“… Recently, many variants, such as parallel ELM (P-ELM) incremental ELM and online sequential ELM(OS-ELM …”
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An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices von Ito, Rei, Tsukada, Mineto, Matsutani, Hiroki

“… Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines. However, retraining or customizing a …”
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Online sequential extreme learning algorithm with kernels for bigdata classification von Pandeeswari, N., Vignesh, D., Pushpalakshmi, R., Varadharajan

“… Despite the parallel and distributed ELM on MapReduce framework able to handle very large scale dataset for bigdata applications, the process of coping up with the rapidly updating data is a challenging one …”
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