関係データ学習

IOT技術:IOT Technology

Protected: Maximization of submodular functions and application of the greedy method (1) Overview of the greedy method and its application to document summarization

Optimization methods for discrete information used in digital transformation, artificial intelligence, and machine learning tasks: application of greedy methods to undermodular function maximization and its use in document summarization tasks
IOT技術:IOT Technology

Protected: Fundamentals of Submodular Optimization (4) Approaches by Linear Optimization and Norm Optimization on a Fundamental Polyhedron

Submodular approach by linear optimization and norm optimization on a base polyhedron in submodular optimization, one of the optimization methods for discrete information used in digital transformation, artificial intelligence, and machine learning tasks.
Symbolic Logic

Knowledge Graph and Semantic Computing

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
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Inductive logic Programming 2018

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
Symbolic Logic

Protected: Fundamentals of Submodular Optimization (3)Algorithm for Submodular Function Minimization Problem Using the Minimum Norm Point of the Fundamental Polyhedron

Algorithm for a submodular function minimization problem using base polyhedral minimum norm points, one of the methods of optimization methods (submodular optimization) for discrete information used in digital transformation, artificial intelligence, and machine learning tasks.
Symbolic Logic

Protected: Fundamentals of Submodular Optimization (2) Basic Properties of Submodular Functions

Three basic properties of submodular functions (normalized, non-negative, symmetric) as a basis for optimization algorithms (submodular optimization) of discrete information for digital transformation, artificial intelligence and machine learning tasks and their application to graph cut maximization and minimization problems
Symbolic Logic

Protected: Fundamentals of Submodular Optimization (1) Definition and Examples of Submodular Functions

Submodular functions (cover functions, graph cut functions, concave functions) and optimization as a basis for discrete information optimization algorithms for digital transformation, artificial intelligence, and machine learning tasks
Symbolic Logic

About the Reasoning Web 2005 Proceedings

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
Symbolic Logic

Protected: Effectiveness of “nursery development” verified by difference in difference

Actual causal inference using the difference-in-differences method, one of the causal inference methods relationship between daycare center development and female employment rate
Symbolic Logic

Protected: Application of causal effect estimation – Causal and adjustment effects of commercial contact

Specific applications of statistical causal inference used in digital transformation, artificial intelligence, and machine learning tasks (causal and adjusted effects of CM contact using average treatment effect ATE and average treatment effect ATT in treatment groups)
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