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...
グラフ理論
アルゴリズム:Algorithms 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
アルゴリズム:Algorithms Geometric approach to data
Geometric approaches to data utilized in digital transformation, artificial intelligence, and machine learning tasks (physics, quantum information, online prediction, Bregman divergence, Fisher information matrix, Bethe free energy function, the Gaussian graphical models, semi-positive definite programming problems, positive definite symmetric matrices, probability distributions, dual problems, topological, soft geometry, topology, quantum information geometry, Wasserstein geometry, Lupiner geometry, statistical geometry)
アルゴリズム:Algorithms Topological handling of data using topological data analysis
Topological handling of data using topological data analysis utilized for digital transformation, artificial intelligence, and machine learning tasks application to character recognition, application to clustering, R, TDA, barcode plots, persistent plots , python, scikit-tda, Death - Birth, analysis of noisy data, alpha complex, vitris-lips complex, check complex, topological data analysis, protein analysis, sensor data analysis, natural language processing, soft geometry, hard geometry, information geometry, Euclidean Spaces
アルゴリズム:Algorithms Protected: Machine Learning with Bayesian Inference – Mixture Models, Data Generation Process and Posterior Distribution
Mixture models and data generation processes and posterior distributions (graphical models, Poisson distribution, Gaussian distribution, Dirichlet distribution, categorical distribution) in machine learning with Bayesian inference used in digital transformation, artificial intelligence, machine learning
アルゴリズム:Algorithms Protected: Unsupervised Learning with Gaussian Processes (2) Extension of Gaussian Process Latent Variable Model
Extension of Gaussian process latent variable models as unsupervised learning by Gaussian processes, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks ,infinite warp mixture models, Gaussian process dynamics models, Poisson point processes, log Gaussian Cox processes, latent Gaussian processes, elliptic slice sampling
アルゴリズム:Algorithms Protected: Information Geometry of Positive Definite Matrices (2) From Gaussian Graphical Models to Convex Optimization
Information geometry of positive definite matrices utilized in digital transformation, artificial intelligence, and machine learning tasks From Gaussian graphical models to convex optimization (chordal graphs, triangulation graphs, dual coordinates, Pythagorean theorem, information geometry, geodesics, sample variance-covariance matrix, maximum likelihood Estimation, divergence, knot space, Riemannian metric, multivariate Gaussian distribution, Kullback-Leibler information measure, dual connection, Euclidean geometry, narrowly convex functions, free energy)
グラフ理論 Protected: Information Geometry of Positive Definite Matrices (1) Introduction of dual geometric structure
Introduction of dual geometric structures as information geometry for positive definite matrices utilized in digital transformation, artificial intelligence, and machine learning tasks (Riemannian metric, tangent vector space, semi-positive definite programming problem, self-equilibrium, Levi-Civita connection, Riemannian geometry, geodesics, Euclidean geometry, ∇-geodesics, tangent vector, tensor quantity, dual flatness, positive definite matrix set)
アルゴリズム:Algorithms Protected: Basic Framework of Statistical Mathematics Theory
Basic framework of statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks regularization, approximation and estimation errors, Höfding's inequality, prediction discriminant error, statistical consistency, learning algorithms, performance evaluation, ROC curves, AUC, Bayes rules, Bayes error, prediction loss, empirical loss
アルゴリズム:Algorithms Protected: Spatial statistics of Gaussian processes, with application to Bayesian optimization
Spatial statistics of Gaussian processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks, and tools ARD, Matern kernelsfor Bayesian optimization GPyOpt and GPFlow and GPyTorch