ICT技術:ICT Technology Artificial Intelligence Technology
Artificial Intelligence technologies used for Digital Transformation (DX), Artificial Intelligence (AI) and Machine Learning (ML) tasks
machine learning
ICT技術:ICT Technology アルゴリズム:Algorithms 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
アルゴリズム:Algorithms 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)
アルゴリズム:Algorithms 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)
アルゴリズム:Algorithms Protected: Conjugate gradient and nonlinear conjugate gradient methods as continuous optimization in machine learning
Conjugate gradient methods as continuous machine learning optimization for digital transformation, artificial intelligence, and machine learning tasks (moment method, nonlinear conjugate gradient method, search direction, inertia term, Polak-Ribiere method, linear search, Wolf condition, Dai-Yuan method, strong Wolf condition, Fletcher-Reeves method, global convergence, Newton method, rapid descent method, Hesse matrix, convex quadratic function, conjugate gradient method, minimum eigenvalue, maximum eigenvalue, affine subspace, conjugate direction method, coordinate descent method)
アルゴリズム:Algorithms Protected: Theory of Noisy L1-Norm Minimization as Machine Learning Based on Sparsity (2)
Theory of noisy L1 norm minimization as machine learning based on sparsity for digital transformation, artificial intelligence, and machine learning tasks numerical examples, heat maps, artificial data, restricted strongly convex, restricted isometric, k-sparse vector, norm independence, subdifferentiation, convex function, regression coefficient vector, orthogonal complementary space
アルゴリズム:Algorithms 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
プログラミング言語:Programming Language Data types and statically and dynamically typed languages in programming
Types of data and statically typed languages and dynamically typed languages (primitive types, heap, Ruby, Python, C#, C++, Java, classes, objects, alias problems, garbage collection, Rust, Borrow checkers, stacks, global variables, value types, reference types, complex types, enumeration types) in programming used for digital transformation, artificial intelligence, machine learning.
python Protected: Applying Neural Networks to Reinforcement Learning Deep Q-Network Applying Deep Learning to Value Assessment
Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Deep Q-Network Prioritized Replay, Multi-step applying deep learning to value assessment Deep Q-Network applying deep learning to value assessment (Prioritized Replay, Multi-step Learning, Distibutional RL, Noisy Nets, Double DQN, Dueling Network, Rainbow, GPU, Epsilon-Greedy method, Optimizer, Reward Clipping, Fixed Target Q-Network, Experience Replay, Average Experience Replay, Mean Square Error, Mean Squared Error, TD Error, PyGame Learning Enviroment, PLE, OpenAI Gym, CNN
Clojure Protected: Network analysis in GraphX Pregel using Clojure
Network analysis in GraphX Pregel using Clojure for digital transformation, artificial intelligence, and machine learning tasks (label propagation, twitter data, community analysis, graph structure analysis, community size, community detection, algorithms, maximum connected components, triangle counting, glittering, Google, Koenigsberg bridge, Euler path)