推論技術:inference Technology Protected: Explainable Artificial Intelligence (12) Model-Independent Interpretation (Global Surrogate)
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人工知能:Artificial Intelligence
推論技術:inference Technology アルゴリズム:Algorithms Protected: Application of Neural Networks to Reinforcement Learning Value Function Approximation, which implements value evaluation as a function with parameters.
Application of Neural Networks to Reinforcement Learning used for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Examples of implementing value evaluation with functions with parameters (CartPole, Q-table, TD error, parameter update, Q-Learning, MLPRegressor, Python)
Clojure Protected: Network Analysis Using Clojure (2)Computing Triangles in a Graph Using Glittering
Network analysis using triangle computation in graphs using Clojure/Glittering for digital transformation, artificial intelligence, and machine learning tasks (GraphX, Pregel API, Twitter dataset, custom triangle count algorithm, message send function, message merge function, outer join, RDD, vertex attributes, Apache Spark, Sparkling, MLlib, Glittering, triangle counting, edge-cut strategy, random-vertex-cut strategy, and social networks, graph parallel computing functions, Hadoop, data parallel systems, RDG, Resilient Distributed Graph, Hama, Giraph)
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 Overview of meta-heuristics and reference books
Overviews
Meta-heuristics can be algorithms used to solve optimization problems. An optimization problem is on...
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