The Experts below are selected from a list of 655380 Experts worldwide ranked by ideXlab platform
Hanchao Yu - One of the best experts on this subject based on the ideXlab platform.
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toselm Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Yiqiang Chen, Xinlong Jiang, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods.
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TOSELM: Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Xinlong Jiang, Junfa Liu, Yiqiang Chen, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods. © 2013 Elsevier B.V.
Yang Gu - One of the best experts on this subject based on the ideXlab platform.
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toselm Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Yiqiang Chen, Xinlong Jiang, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods.
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TOSELM: Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Xinlong Jiang, Junfa Liu, Yiqiang Chen, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods. © 2013 Elsevier B.V.
Yiqiang Chen - One of the best experts on this subject based on the ideXlab platform.
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toselm Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Yiqiang Chen, Xinlong Jiang, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods.
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TOSELM: Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Xinlong Jiang, Junfa Liu, Yiqiang Chen, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods. © 2013 Elsevier B.V.
Xinlong Jiang - One of the best experts on this subject based on the ideXlab platform.
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toselm Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Yiqiang Chen, Xinlong Jiang, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods.
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TOSELM: Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Xinlong Jiang, Junfa Liu, Yiqiang Chen, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods. © 2013 Elsevier B.V.
Junfa Liu - One of the best experts on this subject based on the ideXlab platform.
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TOSELM: Timeliness online sequential extreme learning machine
Neurocomputing, 2014Co-Authors: Yang Gu, Xinlong Jiang, Junfa Liu, Yiqiang Chen, Hanchao YuAbstract:For handling data and training model, existing machine learning methods do not take Timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on Timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with Timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods. © 2013 Elsevier B.V.
