セマンテックウェブ技術:Semantic web Technology KI 2018: Advances in Artificial Intelligence Papers
Machine Learning Technology Artificial Intelligence Technology Natural Language Processing Technology Semantic Web Te...
推論技術:inference Technology
セマンテックウェブ技術:Semantic web Technology 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
Symbolic Logic Integration of logic and rules with probability/machine learning
Integration of logic and rules with machine learning (inductive logic programming, statistical relational learning, knowledge-based model building, Bayesian nets, probabilistic logic learning, hidden Markov models) used for digital transformation, artificial intelligence, and machine learning tasks.
アルゴリズム:Algorithms Protected: Structural learning of graphical models
On learning graph structures from data in Bayesian networks and Markov probability fields (Max-Min Hill Climbing (MMHC), Chow-Liu's algorithm, maximizing the score function, PC (Peter Spirtes and Clark Clymoir) Algorithm, GS (Grow-Shrink) algorithm, SGS (Spietes Glymour and Scheines) algorithm, sparse regularization, independence condition)
アルゴリズム:Algorithms Protected: Calculation method for Gaussian processes based on a lattice arrangement of auxiliary points
Gaussian process method calculations based on lattice-like auxiliary point arrangements in Gaussian process models Kronecker method, Teblitz method, local kernel interpolation, KISS-GP method, an application of stochastic generative models used in digital transformation, artificial intelligence, machine learning
アルゴリズム:Algorithms Protected: A linear summation method and message propagation algorithm for MAP estimation of discrete-state graphical models
MAP estimation using linear programming in a graphical model of discrete states in a stochastic generative model (max-sum diffusion (MSD) algorithm, Generalized MPLP, MPLP algorithm, dual solution of the relaxation problem, dual decomposition, solution by message propagation, separation algorithm, cycle inequality, MAP estimation problem formulated as a linear programming problem)
アルゴリズム:Algorithms Protected: Computational Methods for Gaussian Processes(2)Variational Bayesian Method and Stochastic Gradient Method
Calculations using variational Bayesian and stochastic gradient methods for Gaussian process models, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks Kullback-Leibler information content, Jensen inequality, evidence lower bound function, mini-batch method, evidence lower bound, variational posterior distribution, evidence variational lower bound
Symbolic Logic Small data learning, fusion of logic and machine learning, local/population learning
Small data learning, fusion of logic and machine learning, local/population learning
Machine learning tec...
アルゴリズム:Algorithms Protected: Maximum Propagation Method for Calculating MAP Assignments in Graphical Models
Estimating the maximized state of probability values (MAP assignment) with the maximum propagation method in probabilistic generative models used in digital transformation, artificial intelligence, and machine learningtasks (TRW maximum propagation method, STA condition, maximum propagation method on a factor graph with cycles, maximum propagation on a tree graph, MAP estimation by message propagation)
アルゴリズム:Algorithms Protected: Foundations of Measure Theory for Nonparametric Bayesian Theory
Foundations of measure theory for nonparametric Bayesian theory (independence of random measures, monotone convergence theorem in Laplace functionals, propositions valid with probability 1, Laplace transform of probability distribution, expectation computation by probability distribution, probability distribution, monotone convergence theorem, approximation theorem by single functions, single functions, measurable functions using Borel set families, Borel sets, σ-finite measures, σ-additive families, Lebesgue measures, Lebesgue integrals)