2021-12

微分積分:Calculus

Protected: Sequential Update Type Anomaly Detection by Mixture Distribution Model – Jensen’s Inequality and EM Method

Overview of sequential update anomaly detection using mixture distribution models (Jensen's inequality, EM method), which is the most popular method used for digital transformation and artificial intelligence tasks.
2021.12.09
微分積分:Calculus推論技術:inference Technology機械学習:Machine Learning異常検知・変化検知確率・統計:Probability and Statistics
推論技術:inference Technology

Protected: Anomaly detection using the nearest neighbor method-Dealing with multimodal distributions and the Riemannian metric

Anomaly and change detection by the nearest neighbor method using Riemannian measurement to deal with multimodal data for digital transformation and artificial intelligence tasks.
2021.12.08
推論技術:inference Technology機械学習:Machine Learning異常検知・変化検知確率・統計:Probability and Statistics
異常検知・変化検知

Protected: Anomaly detection using simple Bayesian method -Differences from binary classification

Overview of Simple Bayesian Methods for Multivariate Anomaly/Change Detection for Digital Transformation and Artificial Intelligence Tasks
2021.12.07
異常検知・変化検知確率・統計:Probability and Statistics
最適化:Optimization

Protected: Anomaly detection by T2 method for hoteling-Mahalanobis distance and chi-square distribution

Anomaly and change detection using the T2 method (Mahalanobis distance) of hoteling used in digital transformation and artificial intelligence tasks.
2021.12.06
最適化:Optimization機械学習:Machine Learning異常検知・変化検知確率・統計:Probability and Statistics
最適化:Optimization

Protected: Basic concept of anomaly and change detection – Neyman-Pearson Decision Rule

An Introduction to Machine Learning for Anomaly and Change Detection Used in Digital Transformation and Artificial Intelligence Tasks
2021.12.05
最適化:Optimization機械学習:Machine Learning異常検知・変化検知確率・統計:Probability and Statistics
機械学習:Machine Learning

Protected: Applications of Markov chain Monte Carlo methods (Ising, combinatorial optimization, particle physics)

Examples of Markov Chain Monte Carlo (MCMC) applications in digital transformation , artificial intelligence, and machine learning tasks, such as Ising, combinatorial optimization (traveling salesman problem), and particle physics, are discussed.
2021.12.04
機械学習:Machine Learning確率・統計:Probability and Statistics
地理空間情報処理

Machine Learning Professional Series – Relational Data Learning Post-Reading Notes

Overview of relational data learning to extract the meaning and knowledge behind information used in digital transformation , artificial intelligence , and machine learning tasks.
2021.12.03
地理空間情報処理推論技術:inference Technology機械学習:Machine Learning画像認識技術自然言語処理:Natural Language Processing音声信号認識技術
グラフ理論

What is a Complex Network? A New Approach to Deciphering Complex Relationships Reading Memo

Overview of graph theory for analyzing complex network information used in artificial intelligence tasks (lattices and networks, Bacon and Erdesh numbers, small worlds, Beki rules, contagion transmission pathways, communication networks, neural networks, community networks).
2021.12.02
グラフ理論
C言語

Protected: Applications of Markov chain Monte Carlo methods (Bayesian inference)

Overview of the application of MCMC methods to Bayesian inference for digital transformation , artificial intelligence , and machine learning tasks, and description of various algorithms
2021.12.02
C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
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