幾何学:Geometry

Symbolic Logic

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
Symbolic Logic

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
IOT技術:IOT Technology

Protected: Maximization of submodular functions and application of the greedy method (1) Overview of the greedy method and its application to document summarization

Optimization methods for discrete information used in digital transformation, artificial intelligence, and machine learning tasks: application of greedy methods to undermodular function maximization and its use in document summarization tasks
2022.04.22
IOT技術:IOT Technologyアルゴリズム:Algorithms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確率・統計:Probability and Statistics関係データ学習
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
オンライン学習

Protected: Combinatorial online prediction

Combinatorial, online prediction problems with a set of discrete structures as the decision space for digital transformation , artificial intelligence , and machine learning task utilization.
2022.02.15
オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
オンライン学習

Protected: Online prediction based on randomness

Randomness-based FPL(Follow the Perturbed Leader) Strategy and Gumbel Distribution for Improving Online Predictive Performance for Digital Transformation , Artificial Intelligence and Machine Learning Tasks
2022.02.14
オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
オンライン学習

Protected: Online convex optimization (3) exp concavity and ONS

Convex optimization for online prediction for digital transformation , artificial intelligence , and machine learning tasks (the case of exp concavity and ONS).
2022.02.10
オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
グラフ理論

Structural Learning

  About Structural Learning Learning the structure that data has is important for interpreting what the data is a...
2022.01.22
グラフ理論幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra
データベース技術:DataBase Technology

Protected: Instance recognition and retrieval (2) General image retrieval

Search optimization using tree structure, hashing, sequential quantization, spectral hashing, k-means hashing, etc. for digital transformation and artificial intelligence tasks, and evaluation using mAP and recall@R.
2022.01.02
データベース技術:DataBase Technology幾何学:Geometry微分積分:Calculus検索技術:Search Technology機械学習:Machine Learning
幾何学:Geometry

Fundamentals of Computer Mathematics

Overview of computer mathematics as a basis for artificial intelligence and machine learning techniques, functions, sets, probability, simultaneous equations, differentiation, and integration.
2021.03.17
幾何学:Geometry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線形代数:Linear Algebra集合論:Set theory
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