数学:Mathematics

Symbolic Logic

Protected: Maximum Flow and Graph Cut (3) Inference and Graph Cut in Markov Stochastic Fields

Inference and graph cuts in Markov stochastic fields for graph maximal flow extraction by undermodular optimization, a discrete information optimization method for digital transformation, artificial intelligence, and machine learning tasks
2022.05.04
Symbolic Logicアルゴリズム:Algorithmsグラフ理論幾何学:Geometry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確率・統計:Probability and Statistics関係データ学習
Symbolic Logic

Inductive logic programming 2020-2021 Papers

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
2022.04.30
Symbolic Logicグラフ理論推論技術:inference Technology数理論理学数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習
アルゴリズム:Algorithms

Protected: Maximum Flow and Graph Cutting (2)Maximum Flow Algorithm

Ford-Fulkerson's algorithm and Goldberg-Tarjan's algorithm for the maximum flow problem for directed graphs used in digital transformation, artificial intelligence, and machine learning tasks, pre-flow and push methods, increasing path algorithms, and residual networks
2022.04.29
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関係データ学習
Symbolic Logic

Protected: Maximum flow and graph cut (1) Maximum volume and minimum s-t cut

Application of undermodular optimization, an optimization method for discrete information used in digital transformation, artificial intelligence, and machine learning tasks, to minimum cut and maximum flow problems for directed graphs
2022.04.28
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
Symbolic Logic

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.
2022.04.27
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
Symbolic Logic

Graph Structures for knowledge Representation and Reasoning

Graph Structures for knowledge Representation and Reasoning From Graph Structures for knowledge Representat...
2022.04.24
Symbolic Logicweb技術:web technologyグラフ理論セマンテックウェブ技術:Semantic web Technologyデータベース技術:DataBase Technologyユーザーインターフェース/データビジュアライゼーション推論技術:inference Technology数理論理学検索技術:Search Technology自然言語処理:Natural Language Processing
Symbolic Logic

Inductive logic Programming 2019

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
2022.04.23
Symbolic Logicグラフ理論推論技術: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
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
2022.04.22
IOT技術:IOT Technologyアルゴリズム:Algorithms幾何学:Geometry推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習
アルゴリズム:Algorithms

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
2022.04.21
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
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.
2022.04.20
IOT技術:IOT Technologyアルゴリズム:Algorithmsグラフ理論幾何学:Geometry推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning関係データ学習
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