機械学習:Machine Learning

Protected: Fundamentals of Statistical Causal Search (2) Three Approaches Identifiability

Identifiability of three approaches for the basis of statistical causal search for digital transformation, artificial intelligence, and machine learning tasks (matrix representation of structural equation models and directed acyclic graphs, average causal effects).

2022.03.03

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: Fundamentals of Statistical Causal Inference (2) – Structural Causal Models and Randomized Experiments

Structural causal models and randomized experiments as a basis for statistical causal inference for digital transformation, artificial intelligence, and machine learning tasks.

2022.02.28

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

Iwanami Data Science Series vol.3 “Causal Theory Reading Causality from Real World Data” Reading Memo

Summary Techniques to examine "causal relationships" that are not "correlations" are "causal inference" and "cau...

2022.02.27

推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Fundamentals of statistical causal inference (1) – Definition of causality by counterfactual model and structural equation model

Foundations of Statistical Causal Inference for Digital Transformation , Artificial Intelligence and Machine Learning Tasks: Defining Causality in Counterfactual Models and Structural Equation Models

2022.02.25

推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Algorithms for Network Flow Problems

The solution of the maximum communication volume problem by Ford-Fulkerson's algorithm and its relation to the minimum cut problem, the maximum matching problem for nipartite graphs which is a special case of the maximum flow problem, the general matching problem and the minimum cost flow problem are described.

2022.02.24

アルゴリズム:Algorithmsグラフ理論最適化:Optimization機械学習:Machine Learning

Zen and Buddha Bot as Told by Artificial No-Brains

The early history of the dialogue engine, starting with the importance of dialogue and Eliza to the meaning of words, philosophy by Wittgenstein, meta-literature by James Joyce, Zen thought (the Ten Oxen) for enlightenment, Zen questions and answers and Buddhabot.

2022.02.19

Symbolic Logicweb技術:web technologyセマンテックウェブ技術:Semantic web Technologyチャットボットデータベース技術:DataBase Technology仏教:Buddhism機械学習:Machine Learning禅:Zen自然言語処理:Natural Language Processing

Protected: Combinatorial online prediction

Combinatorial, online prediction problems with a set of discrete structures as the decision space for digital transformation , artificial intelligence , and machine learning task utilization.

2022.02.15

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Online prediction based on randomness

Randomness-based FPL(Follow the Perturbed Leader) Strategy and Gumbel Distribution for Improving Online Predictive Performance for Digital Transformation , Artificial Intelligence and Machine Learning Tasks

2022.02.14

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Online convex optimization (3) exp concavity and ONS

Convex optimization for online prediction for digital transformation , artificial intelligence , and machine learning tasks (the case of exp concavity and ONS).

2022.02.10

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Online Convex Optimization (2) Complementing FTL Strategies with Regularization

Complementing the FTL strategy by introducing regularization techniques (L2 norm) in online prediction for digital transformation , artificial intelligence , and machine learning tasks.

2022.02.09

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics