on-line stochastic optimization

Protected: Distributed processing of on-line stochastic optimization

Distributed online stochastic optimization for digital transformation, artificial intelligence, and machine learning tasks (expected error, step size, epoch, strongly convex expected error, SGD, Lipschitz continuous, gamma-smooth, alpha-strongly convex, Hogwild!), parallelization, label propagation method, propagation on graphs, sparse feature vectors, asynchronous distributed SGD, mini-batch methods, stochastic optimization methods, variance of gradients, unbiased estimators, SVRG, mini-batch parallelization of gradient methods, Nesterov's acceleration method, parallelized SGD)

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Batch Stochastic Optimization – Stochastic Dual Coordinate Descent

Stochastic dual coordinate descent algorithms as batch-type stochastic optimization utilized in digital transformation, artificial intelligence, and machine learning tasks Nesterov's measurable method, SDCA, mini-batch, computation time, batch proximity gradient method, optimal solution, operator norm, maximum eigenvalue , Fenchel's dual theorem, principal problem, dual problem, proximity mapping, smoothing hinge loss, on-line type stochastic optimization, elastic net regularization, ridge regularization, logistic loss, block coordinate descent method, batch type stochastic optimization

アルゴリズム:Algorithmsスパースモデリング幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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