python Protected: Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Poor Reproducibility: Evolutionary Strategies
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機械学習:Machine Learning
python アルゴリズム:Algorithms Protected: Application of Bandit Method (3) Recommendation System
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アルゴリズム:Algorithms Protected: Applications of the Bandit Method (1) Monte Carlo Tree Search
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アルゴリズム:Algorithms Protected: Extension of the Bandit Problem Partial Observation Problem
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アルゴリズム:Algorithms Protected: Extension of the Bandit Problem – Time-Varying Bandit Problem and Comparative Bandit
Time-varying bandit problems and comparative bandits as extensions of bandit problems utilized in digital transformation, artificial intelligence, and machine learning tasks RMED measures, Condorcet winner, empirical divergence, large deviation principle, Borda winner, Coplan Winner, Thompson Extraction, Weak Riglet, Total Order Assumption, Sleeping Bandit, Ruined Bandit, Non-Dormant Bandit, Discounted UCB Measures, UCB Measures, Hostile Bandit, Exp3 Measures, LinUCB, Contextual Bandit
アルゴリズム:Algorithms Protected: Mathematical Properties and Optimization of Sparse Machine Learning with Atomic Norm
Mathematical properties and optimization of sparse machine learning with atomic norm for digital transformation, artificial intelligence, and machine learning tasks L∞ norm, dual problem, robust principal component analysis, foreground image extraction, low-rank matrix, sparse matrix, Lagrange multipliers, auxiliary variables, augmented Lagrangian functions, indicator functions, spectral norm, robust principal component analysis, Frank-Wolfe method, alternating multiplier method in duals, L1 norm constrained squared regression problem, regularization parameter, empirical error, curvature parameter, atomic norm, prox operator, convex hull, norm equivalence, dual norm
アルゴリズム:Algorithms Protected: Definition and Examples of Sparse Machine Learning with Atomic Norm
Definitions and examples in sparse machine learning with atomic norm used in digital transformation, artificial intelligence, and machine learning tasks nuclear norm of tensors, nuclear norm, higher-order tensor, trace norm, K-order tensor, atom set, dirty model, dirty model, multitask learning, unconstrained optimization problem, robust principal component analysis, L1 norm, group L1 norm, L1 error term, robust statistics, Frobenius norm, outlier estimation, group regularization with overlap, sum of atom sets, element-wise sparsity of vectors, groupwise sparsity of group-wise sparsity, matrix low-rankness
アルゴリズム:Algorithms Protected: Upper Bound Minimization Algorithm as Sequential Optimization in Machine Learning
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アルゴリズム:Algorithms Protected: Optimization Using Lagrangian Functions in Machine Learning (3)Alternate Direction Multiplier Method
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アルゴリズム:Algorithms Protected: Optimization using Lagrangian functions in machine learning (1)
Optimization using Lagrangian functions in machine learning for digital transformation, artificial intelligence, and machine learning tasks (steepest ascent method, Newton method, dual ascent method, nonlinear equality-constrained optimization problems, closed truly convex function f, μ-strongly convex function, conjugate function, steepest descent method, gradient projection method, linear inequality constrained optimization problems, dual decomposition, alternate direction multiplier method, regularization learning problems)