hummingbird | Page 138 | Deus Ex Machina

Similarity in global matching (5) Probabilistic approach

Natural Language Similarity for Digital Transformation (DX), Artificial Intelligence (AI) and Machine Learning (ML) Applications. Evaluating ontology similarity by relational pattern matching, a probabilistic approach.

2021.08.16

セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology検索技術:Search Technology機械学習:Machine Learning深層学習:Deep Learning自然言語処理:Natural Language Processing

Similarity in global matching (4) Optimized matching method、EM and PSO

Natural language similarity for Digital Transformation (DX), Artificial Intelligence (AI) and Machine Learning (ML) applications. Similarity evaluation of ontologies by relational pattern matching, Expectation Maximization (EM) and Particle Swarm Optimisation (PSO).

2021.08.15

セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology検索技術:Search Technology機械学習:Machine Learning深層学習:Deep Learning自然言語処理:Natural Language Processing

Similarity in global matching (3) Matching graph patterns by iterative computation

Natural language similarity for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) applications. Graph data matching calculations for ontology similarity extraction.

2021.08.14

セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology検索技術:Search Technology機械学習:Machine Learning深層学習:Deep Learning自然言語処理:Natural Language Processing

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: Graph Neural Networks (1) Overview

An overview of machine learning using graph neural networks for use in digital transformation (DX) and artificial intelligence (AI). On graph convolutional algorithms for chemical combination and natural language processing.

2021.08.10

機械学習:Machine Learning深層学習:Deep Learning

Protected: Theory of noiseless L1-norm minimization(The problem of finding sparse solutions that satisfy linear equations)

Sparse model machine learning and norms for data compression and feature extraction

2021.08.09

スパースモデリング機械学習:Machine Learning

Protected: Machine Learning Based on Sparsity (3) Introduction of Sparsity and L1 Norm

Sparse model machine learning and norms for data compression and feature extraction

2021.08.08

スパースモデリング機械学習:Machine Learning

Protected: Machine Learning Based on Sparsity (2) Machine Learning Basics, Norm and Regularization

Basics of efficient machine learning using sparsity, norm and regularization

2021.08.07

スパースモデリング機械学習:Machine Learning