スパースモデリング

Protected: Statistical Mathematical Theory for Boosting

Statistical and mathematical theory boosting generalized linear model, modified Newton method, log likelihood, weighted least squares method, boosting, coordinate descent method, iteratively weighted least squares method, iteratively reweighted least squares method, IRLS method, weighted empirical discriminant error, parameter update law, Hessian matrix, corrected Newton method, Newton method, Newton method, iteratively reweighted least squares method, IRLS method) used for digital transformation, artificial intelligence, machine learning tasks. iteratively reweighted least square method, IRLS method, weighted empirical discriminant error, parameter update law, Hessian matrix, corrected Newton method, modified Newton method, Newton method, Newton method, link function, logistic loss, logistic loss, boosting algorithm, logit boost, exponential loss, convex margin loss, adaboost, weak hypothesis, empirical margin loss, nonlinear optimization

2023.03.08

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

Protected: Quasi-Newton Methods as Sequential Optimization in Machine Learning (2)Quasi-Newton Methods with Memory Restriction

Quasi-Newton method with memory restriction (sparse clique factorization, sparse clique factorization, chordal graph, sparsity, secant condition, sparse Hessian matrix, DFP formula, BFGS formula, KL divergence, quasi-Newton method, maximal clique, positive definite matrix, positive definite matrix completion, positive define matrix composition, graph triangulation, complete subgraph, clique, Hessian matrix, triple diagonal matrix Hestenes-Stiefel method, L-BFGS method)

2023.03.07

アルゴリズム:Algorithmsスパースモデリング微分積分: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: Applying Neural Networks to Reinforcement Learning Applying Deep Learning to Strategy:Advanced Actor Critic (A2C)

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Implementation of Advanced Actor Critic (A2C) applying deep learning to strategies (Policy Gradient method, Q-learning, Gumbel Max Trix, A3C (Asynchronous Advantage Actor Critic))

2023.03.02

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強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Overview of nu-Support Vector Machines by Statistical Mathematics Theory

Overview of nu-support vector machines by statistical mathematics theory utilized in digital transformation, artificial intelligence, and machine learning tasks (kernel functions, boundedness, empirical margin discriminant error, models without bias terms, reproducing nuclear Hilbert spaces, prediction discriminant error, uniform bounds Statistical Consistency, C-Support Vector Machines, Correspondence, Statistical Model Degrees of Freedom, Dual Problem, Gradient Descent, Minimum Distance Problem, Discriminant Bounds, Geometric Interpretation, Binary Discriminant, Experience Margin Discriminant Error, Experience Discriminant Error, Regularization Parameter, Minimax Theorem, Gram Matrix, Lagrangian Function).

2023.02.23

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: Stochastic coordinate descent as a distributed process for batch stochastic optimization

Stochastic coordinate descent as a distributed process for batch stochastic optimization utilized in digital transformation, artificial intelligence, and machine learning tasks (COCOA, convergence rate, SDCA, γf-smooth, approximate solution of subproblems, stochastic coordinate descent, parallel stochastic coordinate descent, parallel computing process, Communication-Efficient Coordinate Ascent, dual coordinate descent)

2023.02.22

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

Protected: Application of Neural Networks to Reinforcement Learning Policy Gradient, which implements a strategy with a function with parameters.

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Policy Gradient to implement strategies with parameterized functions (discounted present value, strategy update, tensorflow, and Keras, CartPole, ACER, Actor Critoc with Experience Replay, Off-Policy Actor Critic, behavior policy, Deterministic Policy Gradient, DPG, DDPG, and Experience Replay, Bellman Equation, policy gradient method, action history)

2023.02.16

アルゴリズム:Algorithmsグラフ理論スパースモデリングマルチエージェントシステム幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Exp3.P measures and lower bounds for the adversarial multi-armed bandit problem Theoretical overview

Theoretical overview of Exp3.P measures and lower bounds for adversarial multi-arm bandit problems utilized in digital transformation, artificial intelligence, and machine learning tasks cumulative reward, Poly INF measures, algorithms, Arbel-Ruffini theorem, pseudo-riglet upper bounds for Poly INF measures, closed-form expressions, continuous differentiable functions, Audibert, Bubeck, INF measures, pseudo-riglet upper bounds for INF measures, random choice algorithms, optimal order measures, highly probable riglet upper bounds) closed form, continuous differentiable functions, Audibert, Bubeck, INF measures, pseudo-riglet lower bounds, random choice algorithms, measures of optimal order, highly probable riglet upper bounds

2023.02.10

アルゴリズム:Algorithmsオンライン学習スパースモデリングバンディッド問題幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Overview of C-Support Vector Machines by Statistical Mathematics Theory

Support vector machines based on statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks C-support vector machines (support vector ratio, Markov's inequality, probability inequality, prediction discriminant error, one-out-of-two cross checking method, LOOCV, the discriminant, complementarity condition, main problem, dual problem, optimal solution, first order convex optimization problem, discriminant boundary, discriminant function, Lagrangian function, limit condition, Slater constraint assumption, minimax theorem, Gram matrix, hinge loss, margin loss, convex function, Bayes error, regularization parameter)

2023.02.09

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

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)

2023.02.08

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

Theory and algorithms of various reinforcement learning techniques and their implementation in python

Theory and algorithms of various reinforcement learning techniques used for digital transformation, artificial intelligence, and machine learning tasks and their implementation in python reinforcement learning,online learning,online prediction,deep learning,python,algorithm,theory,implementation

2023.02.05

アルゴリズム:Algorithmsオンライン学習グラフ理論スパースモデリング幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra