機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (4) Introducing Sparsity into Relationships

The graph sparsity model, which is used to reduce the dimensionality of graph data and to explain machine learning models, is discussed in terms of introducing sparsity into relationships and graph lasso.

スパースモデリング機械学習:Machine Learning

Protected: Two approaches to language meaning (fusion of symbolic and distributed representations)

Symbolic and vector representation approaches to natural language meaning that can be used for artificial intelligence (AI) and digital transformation (DX) tasks and their integration, content relation recognition, paraphrase recognition, semantic similarity recognition, datasets (RTE, RITE, STS)

機械学習:Machine Learning自然言語処理:Natural Language Processing

Protected: Teaching the meaning of words to a computer (on various language models)

Meaning of Natural Language by WordNet (Dictionary), Distributed Hypothesis, PPMI, Singularity Decomposition (SVD), Word2Vec (Distributed Representation) in Natural Language Processing

機械学習:Machine Learning自然言語処理:Natural Language Processing

Protected: Topic models that capture the individuality of language

Topic models to capture latent meanings behind sentences, differences between various probabilistic approaches and deep learning, supervised LDA, Boltzmann machines,Naive Bayes

機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Strategies in similarity matching methods (7) Improved alignment disambiguation

Alignment disambiguation for optimization of natural language simirality and ontology matching for digital transformation (DX) and artificial intelligence (AI) applications

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

Strategies in similarity matching methods (6) Alignment extraction approach

Optimization and alignment extraction for natural language simirality and ontology matching for digital transformation (DX) and artificial intelligence (AI) applications

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

Strategies in similarity matching methods (5) Tuning approaches

Digital transformation (DX), similarity of natural language for artificial intelligence (AI), tuning by machine learning for ontology matching, stacked generalization, genetic algorithms

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

Strategies in similarity matching methods (4) Learning to sort alignments

Digital transformation (DX), simirality of natural language for artificial intelligence (AI) applications, sorting alignment by machine learning for ontology matching, SVM, decision trees, WHIRL, neural nets, naive Bayes

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

Strategies in similarity matching methods (3) Weighted selection approach

Natural language similarity for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) applications. Similarity evaluation, matching strategies, and weighting strategies for ontologies using relational pattern matching.

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

Strategies in similarity matching methods (1) Overview

Natural language similarity for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) applications. Similarity evaluation of ontologies by relational pattern matching, matching strategies.

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

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