機械学習:Machine Learning

セマンテックウェブ技術:Semantic web Technology

Similarity in global matching (2) Similarity of Mereological Graph Patterns

Natural Language Similarity for Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML) Applications. Similarity evaluation of ontologies by relational pattern matching.
推論技術:inference Technology

Similarity in global matching (1) Overview

Similarity of natural language for the use of digital transformation (DX), artificial intelligence (AI), and machine learning (ML), and similarity of ontology as matching of graph patterns.
セマンテックウェブ技術:Semantic web Technology

Basic Similarity(5) Data Expansion Techniques

Natural Language Similarity for Digital Transformation (DX), Artificial Intelligence (ML), Machine Learning (ML). Similarity for ontology matching using an extended data approach.
推論技術:inference Technology

Basic Similarity (4) Internal structure-based approach

Similarity estimation of natural language for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) applications based on data types and domain elements.
推論技術:inference Technology

Basic Similarity(3) A language-based approach

Linguistic approaches to natural language similarity assessment that can be used for digital transformation (DX), artificial intelligence (AI), and machine learning (ML), including multilingual support.
推論技術:inference Technology

Basic Similarity (2) String-based approach

Summary of various methods for structuring raw text as a basis for natural language processing for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) applications, normalization techniques, substring handling, edit distance, token-based Distance and path comparison
セマンテックウェブ技術:Semantic web Technology

Basic Similarity (1) Overview

Definition and overview of natural language similarity, which is important when applying digital transformation (DX), artificial intelligence (AI), and machine learning (ML), based on ontology matching
アルゴリズム:Algorithms

Explainable Machine Learning

In the first half of Molnar's paper on Explainable Artificial Intelligence, he outlines that explanations can be intrinsic or post-hoc, and in the latter approach, an algorithm is used to construct an explanation from input-output pairs. A good explanation is one that can be compared with counterexamples.
Clojure

one hot vector and category vector with Clojure

Implementation of one-hot-vector and category-vector in Clojure for machine learning applications in natural language processing
機械学習:Machine Learning

Protected: Matrix Decomposition -Extraction of relational features between two objects

Extraction of relationships by machine learning, matrix factorization approach, non-negative matrix factorization
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