2017 International Conference on Networking and Network Applications (NaNA) 2017
DOI: 10.1109/nana.2017.22
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“…SGD is an iterative optimization algorithm that is often used to solve and optimize model parameters of machine learning algorithms. SGD is a deformed form of the gradient descent algorithm, which has been successfully applied to text classification [33] and large-scale sparse machine learning problems in natural language processing [34]- [35]. The gradient is to obtain the partial derivative of the unknown parameters of a multivariate function and obtain the vector composed of these partial derivative functions.…”
Section: ) Fault Classification and Prediction Model Based On Stochastic Gradient Descentmentioning
confidence: 99%
“…SGD is an iterative optimization algorithm that is often used to solve and optimize model parameters of machine learning algorithms. SGD is a deformed form of the gradient descent algorithm, which has been successfully applied to text classification [33] and large-scale sparse machine learning problems in natural language processing [34]- [35]. The gradient is to obtain the partial derivative of the unknown parameters of a multivariate function and obtain the vector composed of these partial derivative functions.…”
Section: ) Fault Classification and Prediction Model Based On Stochastic Gradient Descentmentioning
confidence: 99%