ML

アルゴリズム:Algorithms

Protected: Regret Analysis for Stochastic Banded Problems

Regret analysis for stochastic banded problems utilized in digital transformation, artificial intelligence, and machine learning tasks (sum of equal sequences, gamma function, Thompson extraction, beta distribution, hem probability, Mills ratio, partial integration, posterior sample, conjugate prior distribution, Bernoulli distribution, cumulative distribution function, expected value, DMED measure, UCB measure, Chernoff-Hefding inequality, likelihood, upper bound, lower bound, UCB score, arms)
アルゴリズム:Algorithms

Protected: Regenerate nuclear Hilbert spaces as a basis for kernel methods in statistical mathematics theory.

Regenerate kernel Hilbert spaces as a basis for kernel methods in statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks orthonormal basis, Hilbert spaces, Gaussian kernels, continuous functions, kernel functions, complete spaces, inner product spaces, equivalence classes, equivalence relations, Cauchy sequences, linear spaces, norms, complete inner products
アルゴリズム:Algorithms

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

Protected: Newtonian and Modified Newtonian Methods as Sequential Optimization in Machine Learning

Newton and modified Newton methods (Cholesky decomposition, positive definite matrix, Hesse matrix, Newtonian direction, search direction, Taylor expansion) as continuous machine learning optimization for digital transformation, artificial intelligence and machine learning tasks
アルゴリズム:Algorithms

Protected: What triggers sparsity and for what kinds of problems is sparsity appropriate?

What triggers sparsity and for what kinds of problems is sparsity suitable for sparse learning as it is utilized in digital transformation, artificial intelligence, and machine learning tasks? About alternating direction multiplier method, sparse regularization, main problem, dual problem, dual extended Lagrangian method, DAL method, SPAMS, sparse modeling software, bioinformatics, image denoising, atomic norm, L1 norm, trace norm, number of nonzero elements
アルゴリズム:Algorithms

Protected: Big Data and Bayesian Learning – The Importance of Small Data Learning

Big Data and Bayesian Learning for Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML) Tasks - Importance of Small Data Learning
スパースモデリング

Protected: Theory of Noisy L1-Norm Minimization as Machine Learning Based on Sparsity (1)

Theory of L1 norm minimization with noise as sparsity-based machine learning for digital transformation, artificial intelligence, and machine learning tasks Markov's inequality, Heffding's inequality, Berstein's inequality, chi-square distribution, hem probability, union Bound, Boolean inequality, L∞ norm, multidimensional Gaussian spectrum, norm compatibility, normal distribution, sparse vector, dual norm, Cauchy-Schwartz inequality, Helder inequality, regression coefficient vector, threshold, k-sparse, regularization parameter, inferior Gaussian noise
アルゴリズム:Algorithms

Protected: Application of Neural Networks to Reinforcement Learning (2) Basic Framework Implementation

Implementation of a basic framework for reinforcement learning with neural networks utilized for digital transformation, artificial intelligence and machine learning tasks (TensorBoard, Image tab, graphical, real-time, progress check, wrapper for env. Observer, Trainer, Logger, Agent, Experience Replay, episode, action probability, policy, Epsilon-Greedy method, python)
Clojure

Protected: Statistical analysis and correlation evaluation using Clojure/Incanter

Statistical analysis and correlation evaluation using Clojure for digital transformation, artificial intelligence, and machine learning tasks cumulative probability, confidence interval, standard deviation, population, 95% confidence interval, two-tailed test, z-transform, Fisher z-transform, cumulative distribution function, t-distribution, one-tailed test, and degrees of freedom, sampling error, null hypothesis, alternative hypothesis, hypothesis test, standard score, Pearson's product ratio correlation coefficient, covariance, jittering, lognormal distribution, Beki power, Gibrat's law, histogram
アルゴリズム:Algorithms

Protected: Online-type stochastic optimization for machine learning with AdaGrad and minimax optimization

Online stochastic optimization and AdaGrad for machine learning utilized in digital transformation, artificial intelligence, and machine learning tasks, minimax optimization sparsity patterns, training errors, batch stochastic optimization, online stochastic optimization, batch gradient method, minimax optimality, generalization error, Lipschitz continuity, strong convexity, minimax optimal error, minimax error evaluation, first-order stochastic oracle, stochastic dual averaging method, stochastic gradient descent method, regular terms, Nemirovsky, Yudin, convex optimization method, expected error bound, riglets, semidefinite matrix, mirror image descent method, soft threshold functions
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