Submodular Optimization

Protected: Structural Regularization Learning with Submodular Optimization (3)Formulation of the structural regularization problem with submodular optimization

Application of submodular function optimization, an optimization method for discrete information, to structural regularization problems and formulations using submodular optimization (linear approximation and steepest effect methods, accelerated proximity gradient method, FISTA, parametric submodular minimization, and splitting algorithms)

2022.05.12

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: Structural regularization learning using submodular optimization (2) Structural sparsity obtained from submodular functions

Structural regularization learning (coupled Lasso and Lovász extensions) by structural sparsity obtained from submodular functions in submodular optimization, an optimization method for discrete information used in digital transformation, artificial intelligence, and machine learning tasks.

2022.05.11

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

Protected: Structural regularization learning with submodular optimization (1) Regularization and p-norm review

Review of sparse modeling, regularization and p-norm to consider structural regularization learning with submodular optimization, an optimization technique for discrete information for digital transformation, artificial intelligence and machine learning tasks

2022.05.06

IOT技術:IOT TechnologyStream Data Processingアルゴリズム:Algorithmsグラフ理論データベース技術: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微分積分:Calculus数理論理学:Mathematical logic時系列データ解析最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Maximum Flow and Graph Cut (4) Graphically Representable Submodular Functions

Maximum flow algorithms and pre-flow push methods in graphically representable submodular functions for submodular optimization, an optimization approach for discrete information utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.05.05

IOT技術:IOT TechnologyStream Data Processingアルゴリズム:Algorithmsグラフ理論セマンテックウェブ技術:Semantic web Technology微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic時系列データ解析最適化:Optimization機械学習:Machine Learning

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

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

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

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

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

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.

2022.04.15

Symbolic Logicアルゴリズム:Algorithmsグラフ理論微分積分:Calculus推論技術:inference Technology数理論理学最適化:Optimization確率・統計:Probability and Statistics関係データ学習