微分積分:Calculus

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: Regression Analysis with Support Vector Machines (1)Approach to linear regression problems

Regression problems with linear functions using dual problems with Lagrangian functions with support vector machines utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.05.20

微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: Time series data analysis (2) Filtering Sequential estimation of state and seasonal adjustment model

Prediction of time series using state-space models of time series data utilized in digital transformation, artificial intelligence, and machine learning; interpolation, parameter estimation, and analysis of store sales using component decomposition and standard seasonal adjustment models.

2022.05.18

IOT技術:IOT TechnologyStream Data Processing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

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 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

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