グラフ理論

Dream of a realistic SimCity

Integration of simulation and machine learning technologies used for digital transformation, artificial intelligence, and machine learning tasks; application of SimCity to the real world using emulation and machine learning

2022.06.15

アルゴリズム:Algorithmsグラフ理論コンピューターシミュレーション微分積分:Calculus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線形代数:Linear Algebra

Protected: kernel function

General kernel functions in support vector machines used in digital transformation (DX), artificial intelligence (AI), and machine learning, and kernel functions on probabilistic, string, and graph-type data

2022.06.10

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning画像認識技術異常検知・変化検知確率・統計:Probability and Statistics音声信号認識技術

Protected: Support vector machines for unsupervised learning

Application of support vector machines for digital transformation, artificial intelligence, and machine learning tasks (1-class SVM with nu-SV classification algorithm for unsupervised classification used for anomaly detection)

2022.06.03

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning画像認識技術確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: Simulation, Data Assimilation, and Emulation

Fusion of extrapolation (deduction) estimation using simulation and interpolation (induction) estimation using machine learning (simulation assimilation and emulation using DNN, etc.) for digital transformation, artificial intelligence and machine learning tasks

2022.05.25

Stream Data Processingアルゴリズム:Algorithmsグラフ理論シミュレーション強化学習微分積分:Calculus時系列データ解析最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Differences between hidden Markov models and state-space models and parameter estimation for state-space models

Differences between state-space models, Bayesian models, and hidden Markov models used in digital transformation, artificial intelligence, and machine learning tasks, and parameter estimation for state-space models

2022.05.21

IOT技術:IOT TechnologyStream Data Processingアルゴリズム:Algorithmsグラフ理論微分積分:Calculus推論技術:inference Technology時系列データ解析最適化:Optimization機械学習:Machine Learning異常検知・変化検知確率・統計:Probability and Statistics

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

Knowledge Graphs and Big Data Processing

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

2022.05.08

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

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