Approach

Protected: Support Vector Machines for Weak Label Learning (2) Multi-Instance Learning

Extension of support vector machines utilized for digital transformation, artificial intelligence, and machine learning tasks; multi-instance learning approach with SVMs for weak-label learning problems (mi-SVM, MI-SVM)

2022.09.02

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus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線形代数:Linear Algebra自然言語処理:Natural Language Processing音声信号認識技術

Hardware approaches to machine learning – FPGA, optical computing, quantum computing

Different approaches in hardware used for digital transformation, artificial intelligence and machine learning - FPGAs, optical computing and quantum computing

2022.08.01

アルゴリズム:Algorithmsコンピューターハードウェア機械学習:Machine Learning

Protected: Non-parametric Bayesian and clustering(1)Dirichlet distribution and infinite mixture Gaussian model

Analysis with a mixed Gaussian model that extends the Dirichlet distribution to infinite dimensions as a nonparametric Bayesian approach in stochastic generative models used in digital transformation artificial intelligence, and machine learning

2022.06.28

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集合論:Set theory

Protected: Regression Analysis with Support Vector Machines (2) Approach to Nonlinear Regression Problems

Approaches to nonlinear regression problems with support vector machines (quantile regression, kernel quantile regression, heterogeneous distribution models, ε-insensitive loss functions) utilized in digital transformation, artificial intelligence, and machine learning tasks.

2022.05.27

アルゴリズム:Algorithms幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning画像認識技術自然言語処理:Natural Language Processing関係データ学習音声信号認識技術

Protected: Statistical Causal Search – Extended Approach

Extension of LiNGAM approach assumptions (linearity, acyclicity, non-Gaussianity) in statistical causal inference used in digital transformation , artificial intelligence , and machine learning tasks

2022.03.15

Symbolic Logicグラフ理論推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM with unobserved common cause (2) Approach to model unobserved common cause as a sum

LiNGAM approach to modeling unobserved common causes as sums to statistical causal inference for digital transformation, artificial intelligence , and machine learning tasks

2022.03.14

グラフ理論ベイズ推定推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM (4)Estimation of LiNGAM model (2)An approach using regression analysis and evaluation of independence

Application of LiNGAM estimation with an iterative regression distribution and independence assessment approach to statistical causal inference for use in digital transformation , artificial intelligence, and machine learning

2022.03.10

Symbolic Logicグラフ理論推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: New Developments in Reinforcement Learning (1) – Reinforcement Learning with Risk Indicators

Different approaches (regular process TD learning, RDPS methods) and implementations (Monte Carlo, analytical methods) in risk-aware reinforcement learning methods for digital transformation , artificial intelligence , and machine learning tasks.

2022.02.03

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics