機械学習:Machine Learning

微分積分:Calculus

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.
推論技術:inference Technology

Protected: Extending the topic model (using other information) (1) Combined topic model and corresponding topic model

Create a topic model with auxiliary information to be used for digital transformation and artificial intelligence tasksJoining / Corresponding Topic Model Overview
機械学習:Machine Learning

Topic Model Theory and Implementation

A topic model is a probability generation model for extracting topics from sentences, which is one of the natural language processing technologies used in digital transformation and artificial intelligence tasks.
微分積分:Calculus

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

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

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

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

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).
機械学習:Machine Learning

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.
微分積分:Calculus

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.
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