スパースモデリング

Protected: Maximization of submodular functions and application of the greedy method (2) Sensor placement problem and active learning problem

Application of submodular function maximization and greedy methods to sensor placement and active learning problems in submodular optimization, a method of optimization of discrete information used in digital transformation, artificial intelligence, and machine learning tasks.

Symbolic Logicグラフ理論スパースモデリング幾何学:Geometry推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

Protected: Fundamentals of Submodular Optimization (5) Lovász Extension and Multiple Linear Extension

Interpretation of submodularity using Lovász extensions and multiple linear extensions as a basis for submodular optimization, an approach to discrete information used in digital transformation, artificial intelligence, and machine learning tasks

アルゴリズム:Algorithmsグラフ理論スパースモデリング微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (11) Practical Examples of SVD, PMD, and NMF with R

Sparse Machine Learning, Matrix Decomposition (SVD, PMD, NMF) with R for Digital Transformation (DX), Artificial Intelligence (AI), Solid Line, BiocManager, PMA

スパースモデリング機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (10) Use of matrix data decomposition

Machine learning for use in digital transformation (DX) and artificial intelligence (AI), application of matrix data analysis to sparse machine learning, SVD, PMD, NMF, LSA, LSI, PCA, LDA

スパースモデリング機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (9) Basics of Matrix Data Decomposition

Machine learning techniques that can be used for digital transformation (DX) and artificial intelligence (AI), fundamentals of applying matrix data to machine learning, singularity decomposition, low-rank matrix approximation

スパースモデリング機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (8) Sparsity of Time Transitions

Application of machine learning techniques and sparse modeling to time-varying information (changes in customers' purchasing interests)

スパースモデリング機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (7) Image Processing and Sparsity (Application of Sparse Land Model)

Application of sparse models, one of the machine learning techniques applicable to artificial intelligence (AI) and digital transformation (DX), to image processing (denoising, object extraction, homography transformation, etc.)

スパースモデリング機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (6) Image Processing and Sparsity (Overview of Machine Learning for Signal Processing)

Overview of Sparse Models for Machine Learning of Image Information for Artificial Intelligence (AI) and Digital Transformation (DX), JPEG, DCT, Sparse Land Model

スパースモデリング機械学習:Machine Learning

Protected: Sparse modeling and multivariate analysis (5) Graphical lasso and its application (anomaly detection, etc.)

Graph sparse models used for dimensionality reduction of graph data and explanation of machine learning models, introduction of sparsity to relations and its application to graphical lasso and anomaly detection etc.

スパースモデリング機械学習: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

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