首页|New Computational Intelligence Findings from Zhejiang Wanli University Outlined (A Generalized Nesterov’s Accelerated Gradientincorporated Non-negative Latent- factorization-of-tensors Model for Efficient Representation To Dynamic Qos Data)
New Computational Intelligence Findings from Zhejiang Wanli University Outlined (A Generalized Nesterov’s Accelerated Gradientincorporated Non-negative Latent- factorization-of-tensors Model for Efficient Representation To Dynamic Qos Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning - Computational Intelligence are discussed ina new report. According to news re porting originating in Ningbo, People’s Republic of China, by NewsRxjournalists , research stated, “Dynamic Quality-of-Service (QoS) data can be efficiently rep resented bya Non-negative Latent-factorization-of-tensors model, which relies o n a Non-negative and MultiplicativeUpdate on Incomplete Tensors (NMU-IT) algori thm. Nevertheless, NMU-IT frequently encounters slowconvergence and inefficient hyper-parameters selection.”
NingboPeople’s Republic of ChinaAsiaComputational IntelligenceMachine LearningZhejiang Wanli University
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