ベイズ推定 Machine Learning Professional Series Statistical Learning Theory Reading Notes
Summary
The theory of statistical properties of machine learning algorithms can be used to theoretically elucid...
機械学習:Machine Learning
ベイズ推定 アルゴリズム:Algorithms Protected: Support vector machines for unsupervised learning
Application of support vector machines for digital transformation, artificial intelligence, and machine learning tasks (1-class SVM with nu-SV classification algorithm for unsupervised classification used for anomaly detection)
IOT技術:IOT Technology Protected: State Space Modeling with R – using dlm and KFAS (1) Basic analysis using dlm
Analysis using dlm on real data (quarterly sales volume data of products) in the analysis of time-series data (on data prediction and filtering and smoothing) used in digital transformation, artificial intelligence , and machine learning tasks.
IOT技術:IOT Technology Weather Forecasting and Data Science
Weather forecasting and data assimilation for simulation and data science integration for digital transformation, artificial intelligence, and machine learning task utilization
Symbolic Logic Uncertainty Reasoning for the Semantic Web 2
Machine Learning Technology Artificial Intelligence Technology Natural Language Processing Technology Semantic Web Te...
機械学習:Machine Learning Machine Learning Professional Series “Theory and Algorithms for Bandit Problems” Reading notes
Machine Learning Professional Series "Theory and Algorithms for Bandit Problems" Reading notes
The Bandit Proble...
アルゴリズム:Algorithms Protected: Regression Analysis with Support Vector Machines (2) Approach to Nonlinear Regression Problems
Approaches to nonlinear regression problems with support vector machines (quantile regression, kernel quantile regression, heterogeneous distribution models, ε-insensitive loss functions) utilized in digital transformation, artificial intelligence, and machine learning tasks.
IOT技術:IOT Technology Protected: Time series data analysis (3)Filtering of nonlinear and non-Gaussian state space models (e.g. particle filter)
Filtering and smoothing of nonlinear and non-Gaussian state-space models using particle filters in the analysis of time-series data with state-space models for digital transformation, artificial intelligence, and machine learning tasks
C/C++ Programming Technology Overview
About Programming Technology Overview
This section provides an overview of programming languages. The...
Stream Data Processing Protected: Simulation, Data Assimilation, and Emulation
Fusion of extrapolation (deduction) estimation using simulation and interpolation (induction) estimation using machine learning (simulation assimilation and emulation using DNN, etc.) for digital transformation, artificial intelligence and machine learning tasks