人工知能:Artificial Intelligence

Machine Learning Prootional Series – Support Vector Machines Reading Notes

Reading notes for a reference book on support vector machines (SVMs), a supervised learning pattern recognition model used for classification and regression in digital transformation, artificial intelligence, and machine learning tasks.

2021.12.12

機械学習:Machine Learning

Protected: Anomaly Detection in Directional Data – Analysis Using Von Mises Fisher Distribution and Chi-Square

Explanation of a method that uses the von Mises Fisher distribution from directional data in anomaly detection technology used in digital transformation and artificial intelligence tasks.

2021.12.11

微分積分:Calculus推論技術:inference Technology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

Protected: Anomaly detection using support vector data description method-Biangulation problems and Lagrangian functions and data cleansing

Anomaly Detection and Data Cleansing Using Support Vector Description Method with Kernel Tricks for Digital Transformation and Artificial Intelligence Tasks

2021.12.10

推論技術:inference Technology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

Anomaly and Change Detection Technologies

An overview of various machine learning techniques for anomaly and change detection used in digital transformation and artificial intelligence tasks

2021.12.09

微分積分:Calculus推論技術:inference Technology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

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

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

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

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

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

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