関係データ学習

Symbolic Logic

Protected: Basics of Statistical Causal Effects (3)Operating Variable Method and Summary

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グラフ理論

Machine Learning Professional Series – Gaussian Processes and Machine Learning Reading Notes

Summary A Gaussian Process (GP) is a nonparametric regression and classification method based on probability th...
Symbolic Logic

Protected: Quasi-experimental design – how to derive causal relationships from observed data

How to verify causality for digital transformation, artificial intelligence , and machine learning tasks by first having the data for causal inference and then verifying causality from there.
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.
グラフ理論

Machine Learning Professional Series “Graphical Models” reading notes

Summary Bayesian estimation can be one of the statistical methods for interpreting data and learning models from...
グラフ理論

Nonparametric Bayesian and Gaussian Processes

Overview Nonparametric Bayes is a method of Bayesian statistics, an "old and new technique" that was already theo...
Symbolic Logic

Inductive logic Programming 2009 Papers

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
機械学習:Machine Learning

Protected: Higher-order relational data – an overview of tensor data processing

Tensor data processing to analyze relationships between three or more objects for use in digital transformation and artificial intelligence tasks.
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