数理論理学:Mathematical logic

Overview of Set Theory and Reference Books

What are sets for digital transformation, artificial intelligence, and machine learning task utilization for first-time learners Cantor's set theory, intuitionistic logic, quantum logic, topological spaces, Grothendieck, Goeter, Cohen, unreachable numbers, measurable numbers, axiom of determination, Frenkel's substitution axiom, von Neumann's regularity axiom, axiomatic set theory, BG set theory, Zermelo set theory, Cantor's diagonal theory reading notes

2022.09.18

数理論理学:Mathematical logic集合論:Set theory

Protected: Overview of Gaussian Processes(4)Hyperparameter Estimation and Generalization of Gaussian Process Regression

Hyperparameter estimation using the gradient descent method of Gaussian process regression for stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (SCG method, L-BFGS method, global solution using MCMC)

2022.07.19

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分: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線形代数:Linear Algebra

Protected: Overview of Gaussian Processes(3)Gaussian Process Regression Model

Computation and optimization of regression models and predictive distributions using Gaussian processes, which are dimensionless stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks

2022.07.11

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分: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線形代数:Linear Algebra

Protected: Model selection and regularization path tracking (1) Cross-validation method

Cross-validation methods (k-partition cross-validation and one-out cross-validation) for selecting hyper-parameters such as regularization parameters for support vector machines utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.07.08

アルゴリズム:Algorithms数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Machine Learning Professional Series Statistical Learning Theory Reading Notes

Summary The theory of statistical properties of machine learning algorithms can be used to theoretically elucid...

2022.06.04

ベイズ推定微分積分: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

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: 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