Symbolic Logic

Protected: Discrepancy between correlation (regression coefficient) and causation (intervention effect)

Differences between regression coefficients and intervention effect values for digital transformation, artificial intelligence , and machine learning tasks.

2022.03.22

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

Machine Learning Professional Series “Submodular Optimization and Machine Learning” reading notes

Machine Learning Professional Series "Submodular Optimization and Machine Learning" reading notes Submodular f...

2022.03.21

Symbolic Logicアルゴリズム:Algorithmsグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

From Inductive logic Programming 2011 Proceedings

Machine Learning Technology Artificial Intelligence Technology Natural Language Processing Technology Semantic Web Te...

2022.03.20

Symbolic Logicアルゴリズム:Algorithmsエキスパートシステム:expertsystemグラフ理論セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology最適化:Optimization検索技術:Search Technology

Protected: Causal InferenceIntroduction(2)Stratified Analysis and Regression Modeling

Theory and practice of causal inference through analysis by stratified analysis and regression models for statistical causal estimation used in digital transformation , artificial intelligence , and machine learning tasks

2022.03.18

Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Introduction to Causal Inference (1) Confounding Factors and Randomized Experiments

Introduction to statistical causal inference (randomized experiments controlling for confounding factors to distinguish between causality and pseudo-correlation)

2022.03.17

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Statistical Causal Search – Extended Approach

Extension of LiNGAM approach assumptions (linearity, acyclicity, non-Gaussianity) in statistical causal inference used in digital transformation , artificial intelligence , and machine learning tasks

2022.03.15

Symbolic Logicグラフ理論推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

From the Proceedings of Inductive logic Programming 2010

Machine Learning Technology Artificial Intelligence Technology Natural Language Processing Technology Semantic We...

2022.03.13

Symbolic Logicアルゴリズム:Algorithmsエキスパートシステム:expertsystemグラフ理論セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology最適化:Optimization検索技術:Search Technology

Protected: LiNGAM (4)Estimation of LiNGAM model (2)An approach using regression analysis and evaluation of independence

Application of LiNGAM estimation with an iterative regression distribution and independence assessment approach to statistical causal inference for use in digital transformation , artificial intelligence, and machine learning

2022.03.10

Symbolic Logicグラフ理論推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM (3)Estimation of LiNGAM model (1)Approach using independent component analysis and regression analysis

Estimation of LiNGAM models using independent component analysis (Hungarian method) and regression analysis (adaptive Lasso) for probabilistic causal search for digital transformation and artificial intelligence task applications

2022.03.09

Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Inductive logic Programming 2009 Papers

Machine Learning Technology Artificial Intelligence Technology Natural Language Processing Technology Semantic Web Te...

2022.03.06

Symbolic Logicアルゴリズム:Algorithmsエキスパートシステム:expertsystemグラフ理論推論技術:inference Technology最適化:Optimization検索技術:Search Technology関係データ学習