アルゴリズム:Algorithms Protected: Regression analysis using Clojure (2) Multiple regression model
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微分積分:Calculus
アルゴリズム:Algorithms アルゴリズム:Algorithms Protected: Optimal arm bandit and Bayes optimal when the player’s candidate actions are large or continuous(1)
Optimal arm bandit and Bayes optimal linear curl, linear bandit, covariance function, Mattern kernel, Gaussian kernel, positive definite kernel function, block matrix, inverse matrix formulation, prior simultaneous probability density, Gaussian process, Lipschitz continuous, Euclidean norm, simple riglet, black box optimization, optimal arm identification, regret, cross checking, leave-one-out cross checking, continuous arm bandit
アルゴリズム:Algorithms Protected: Sparse machine learning based on trace-norm regularization
Sparse machine learning based on trace norm regularization for digital transformation, artificial intelligence, and machine learning tasks PROPACK, random projection, singularity decomposition, low rank, sparse matrix, update formula for proximity gradient, collaborative filtering, singular value solver,. Trace norm, prox action, regularization parameter, singular value, singular vector, accelerated proximity gradient method, learning problem with trace norm regularization, semidefinite matrix, square root of matrix, Frobenius norm, Frobenius norm squared regularization, Torres norm minimization, binary classification problem, multi-task learning group L1 norm, recommendation systems
アルゴリズム:Algorithms Protected: Optimality conditions for constrained inequality optimization problems in machine learning
Optimality conditions for constrained inequality optimization problems in machine learning used in digital transformation, artificial intelligence, and machine learningtasks duality problems, strong duality, Lagrangian functions, linear programming problems, Slater conditions, principal dual interior point method, weak duality, first order sufficient conditions for convex optimization, second order sufficient conditions, KKT conditions, stopping conditions, first order optimality conditions, valid constraint expressions, Karush-Kuhn-Tucker, local optimal solutions
アルゴリズム:Algorithms Protected: Fundamentals of convex analysis in stochastic optimization (1) Convex functions and subdifferentials, dual functions
Convex functions and subdifferentials, dual functions (convex functions, conjugate functions, Young-Fenchel inequality, subdifferentials, Lejandre transform, subgradient, L1 norm, relative interior points, affine envelope, affine set, closed envelope, epigraph, convex envelope, smooth convex functions, narrowly convex functions, truly convex closed functions, closed convex closed functions, execution domain, convex set) in basic matters of convex analysis in stochastic optimization used for Digital Transformation, Artificial Intelligence, Machine Learning tasks.
アルゴリズム:Algorithms Protected: Image feature extraction and missing value inference in linear dimensionality reduction models in Bayesian inference
Image feature extraction and missing value inference (missing image information recovery, defect value interpolation, variational inference, unfilled questionnaires, unfilled profile information, multiple sensor integration, linear dimensionality compression algorithm, image lossy compression) in linear dimensionality reduction model in Bayesian inference used for digital transformation, artificial intelligence, machine learning tasks.
アルゴリズム:Algorithms Protected: Explainable Machine Learning (17) Counterfactual Explanations
Explanation of machine learning results by counterfactual explanations utilized in digital transformation, artificial intelligence, and machine learning tasks Anchor, Growing Spheres algorithm, Python, Alibi, categorical features, Rashomon effect, LIME, fully coupled neural networks, counterfactual generation algorithms, Euclidean distance, central absolute deviation, Nelder-Mead method, causal semantics, causes
アルゴリズム:Algorithms Protected: Overview of Weaknesses and Countermeasures in Deep Reinforcement Learning and Two Approaches to Improve Environment Recognition
An overview of the weaknesses and countermeasures of deep reinforcement learning utilized in digital transformation, artificial intelligence, and machine learning tasks and two approaches of improving environmental awareness Mixture Density Network, RNN, Variational Auto Encoder, World Modles, Expression Learning, Strategy Network Compression, Model Free Learning, Sample-Based Planning Model, Dyna, Simulation-Based, Sample-Based, Gaussian Process, Neural Network, Transition Function, Reward Function) World Modles, Representation Learning, Strategy Network Compression, Model-Free Learning, Sample-Based Planning Model, Dyna, Simulation-Based, Sample-Based, Gaussian Process, Neural Network, Transition Function, Reward Function, Simulator , learning capability, transition capability
Clojure Protected: Regression analysis using Clojure (1) Single regression model
Regression analysis using Clojure for digital transformation, artificial intelligence, and machine learning tasks (1) Single regression model (coefficient of determination R2, correlation coefficient R, variance of residuals, variance, mean square error, explanatory variables, goodness of fit, linear regression model, dependent variable, independent variable, modeling error, heteroscedasticity, residual plot, regression line function, linear equation, regression model,incanter)
アルゴリズム:Algorithms Protected: Thompson Sampling, linear bandit problem on a logistic regression model
Thompson sampling, linear bandit problem on logistic regression models utilized in digital transformation, artificial intelligence, and machine learning tasks (Thompson sampling, maximum likelihood estimation, Laplace approximation, algorithms, Newton's method, negative log posterior probability, gradient vector, Hesse matrix, Laplace approximation, Bayesian statistics, generalized linear models, Lin-UCB measures, riglet upper bound)