微分積分:Calculus

Protected: Extension of topic models (adding structure to topics) Correlation topic model, slingshot distribution model with hierarchical structure, probabilistic latent semantic visualization with low-dimensional spatial structure

Overview of topic models with structure in correlated topics used in digital transformation and artificial intelligence tasks (correlated topic model, slingshot distribution model with hierarchical structure, probabilistic latent meaning visualization with low-dimensional spatial structure)

微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: Extending topic models (using other information as well) (2) Noisy correspondence topic model, author topic model, topic tracking model

Among topic models that rely on auxiliary information for digital transformation and artificial intelligence tasks, we will discuss noisy topic models, author topic models, and topic tracking models.

微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: Change detection by density ratio estimation – Detection of structural changes using the Kullback-Leibler density ratio estimation method

Detecting structural changes using the Kullback-Leibler density ratio estimation method for digital transformation and artificial intelligence tasks.

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 by density ratio estimation – Anomaly Estimation from Unsupervised Data Using the Kullback-Leibler Density Ratio Estimation Method

Among the anomaly/change detection techniques used in digital transformation and artificial intelligence tasks, I will introduce a method for anomaly detection using probability density ratio for unsupervised data ,Kullback-Leibler density ratio estimation method

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 sparse structure learning- Graph models and regularization that link broken dependencies between variables to anomalies.

Graph models and regularization that link broken dependencies between variables to anomalies.

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: Change detection using subspace method -Singular spectral transform method for time series data

Singular spectral transform (SVD) method for extracting change points from time series data for digital transformation and artificial intelligence tasks.

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 by Gaussian Process Regression -Output anomaly detection for input, application to design of experiments

Detection of how much the output corresponding to the input is abnormal by Gaussian process regression, one of the most versatile methods of anomaly detection used in digital transformation and artificial intelligence tasks (application to design of experiments).

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 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.

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

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

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.

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

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