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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

Overview of statistical learning theory (explanation without mathematical formulas)

On the theory of statistical properties of machine learning algorithms The theory of the statistical properties...
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)
アルゴリズム:Algorithms

Protected: Optimization Using Lagrangian Functions in Machine Learning (2) Extended Lagrangian Method

Overview of optimization methods and algorithms using extended Lagrangian function methods in machine learning for digital transformation, artificial intelligence, and machine learning tasks proximity point algorithm, strongly convex, linear convergence, linearly constrained convex optimization problems, strong duality theorem, steepest descent method, Moreau envelope, conjugate function, proximity mapping, dual problem, dual ascent method, penalty function method, barrier function method
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Overview and implementation of stochastic optimization in machine learning

Overview of Stochastic Optimization in Machine Learning Stochastic optimization represents a method of solvin...
アルゴリズム:Algorithms

Protected: Fundamentals of convex analysis in stochastic optimization (2) Fenchel’s dual theorem, proximity maps and strongly convex functions

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On the Road to Karatsu, Hirado, Sasebo and Nagasaki

Summary Travel is an act for human beings to visit new places and experience different cultures and histories. T...
哲学:philosophy

The Self-Reflections of Marcus Aurelius

Summary Marcus Aurelius was an ancient Roman philosopher and emperor and one of the last great masters of Stoic p...
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