Lasso

Protected: Reinforcement Learning with Function Approximation (2) – Function Approximation of Value Functions (For Online Learning)

Theory of function approximation online methods gradient TD learning, least-squares based least-squares TD learning (LSTD), GTD2)for reinforcement learning with a huge number of states used in digital transformation , artificial intelligence , and machine learning tasks, and regularization with LASSO.

2022.01.28

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Protected: Sparse Modeling and Multivariate Analysis (3) Practical use of lasso with glmnet and genlasso

About sparse models used for data dimensionality reduction and explanation of machine learning models, implementation of Lasso using R, genlasso and glmnet.

2021.08.13

スパースモデリング機械学習:Machine Learning

Protected: Sparse modeling and multivariate analysis (2) Sparse estimation using lasso and computational methods

An overview of Lasso and its estimation and computational methods for sparse models, which are used to reduce the dimensionality of data and to explain machine learning models.

2021.08.12

スパースモデリング機械学習:Machine Learning

Protected: Sparse modeling and multivariate analysis (1) Differences in model fit and prediction performance and lasso

Artificial Intelligence (AI), Machine Learning (ML), especially sparse modeling (L2 regularization (ridge regression)) and L1 regularization (lasso), which can be used as explainable machine learning, from the point of view of model fitting.

2021.08.11

スパースモデリング機械学習:Machine Learning

Protected: Explainable Machine Learning (6) Interpretable Model (RuleFit)

Overview of interpretable models using RuleFit, explainable machine learning used in digital transformation (DX), artificial intelligence (AI), and machine learning (ML) (about RuleFit, a model generated by Lasso and ensemble learning)

2021.07.22

機械学習:Machine Learning