機械学習:Machine Learning

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
Stream Data Processing

Protected: PF for fast processing of streamed data and large amounts of data: Apache Spark Overview

Overview of ApacheSpark, an open source platform used for digital transformation, artificial intelligence, and machine learning tasks to process streamed and massive data at high speed
2022.04.19
Stream Data Processingweb技術:web technologyオンライン学習強化学習時系列データ解析機械学習:Machine Learning異常検知・変化検知非同期/並行処理:Asynchronous/parallel processing
Symbolic Logic

Inductive logic Programming 2018

In the previous article, we discussed ILP2017. In this issue, I will discuss ILP2018 held in Ferrara, Italy...
2022.04.16
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
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
2022.04.14
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
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
2022.04.13
Symbolic Logicアルゴリズム:Algorithms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
Symbolic Logic

About the Reasoning Web 2005 Proceedings

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
2022.04.10
Symbolic Logicweb技術:web technologyエキスパートシステム:expertsystemグラフ理論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:Mathematical logic最適化:Optimization機械学習:Machine Learning関係データ学習
Symbolic Logic

Inductive logic Programming 2017 Papers

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
2022.04.09
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
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
2022.04.08
Symbolic Logicグラフ理論推論技術: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確率・統計:Probability and Statistics関係データ学習
Symbolic Logic

Protected: Estimating Bunt Effects Using Propensity Scores

Estimating baseball bunt effects using propensity scores as an application of causal inference for digital transformation, artificial intelligence, and machine learning tasks.
2022.04.07
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
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