機械学習:Machine Learning

アルゴリズム:Algorithms

Protected: Support Vector Machines for Weak Label Learning (1) Semi-supervised Learning

Weak label learning (semi-supervised learning where label information is given only for a subset of training cases) as an application of support vector machines utilized in digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Application of Variational Bayesian Algorithm to Matrix Decomposition Models

Variational Bayesian learning and empirical variational Bayesian learning algorithms for matrix factorization models as computational methods for stochastic generative models utilized in digital transformation , artificial intelligence , and machine learning tasks
アルゴリズム:Algorithms

Protected: Computation of graphical models without hidden variables

Maximum likelihood, Bayesian, and variational computations of graphical models without hidden variables in probabilistic generative models utilized in digital truss formation, artificial intelligence, and machine learning tasks, learning by the pseudolikelihood function, Bethe approximation, parameter estimation by TRW upper bound, variational methods, entropy functions, IPF algorithm, MAP estimators
アルゴリズム:Algorithms

Protected: Non-patometric Bayes and clustering (2) Stochastic model of partitioning and Dirichlet processes

Clustering using nonparametric Bayes, one of the applications of probabilistic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks (Chinese restaurant process and Dirichlet process and concentration parameter estimation, bar-folding process)
アルゴリズム:Algorithms

stochastic optimization

Stochastic optimization methods for solving large-scale learning problems on large amounts of data used in digital transformation, artificial intelligence, and machine learning tasks supervised learning and regularization, basics of convex analysis, what is stochastic optimization, online stochastic optimization, batch stochastic optimization, stochastic optimization in distributed environments
アルゴリズム:Algorithms

Statistical Learning Theory

Theory on statistical properties of machine learning algorithms utilized in digital transformation, artificial intelligence, and machine learning tasks (law of large uniform numbers, universal kernel, discriminant fitting loss)
アルゴリズム:Algorithms

Continuous optimization in machine learning

Sequential optimization, an important computational method for constructing machine learning algorithms used in digital transformation artificial intelligence and machine learning tasks
アルゴリズム:Algorithms

Protected: Gaussian Processes – The Advantages of Functional Clouds and Their Relationship to Regression Models, Kernel Methods, and Physical Models

Gaussian Processes as Applications of Stochastic Generative Models for Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML) Tasks Miscellaneous Function Clouds Advantages and Regression Models and their Relationship to Kernel Methods and Physical Models
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Protected: Structured Support Vector Machines

SVM structure learning and parsing using the deletion plane method algorithm on support vector machines utilized for digital transformation, artificial intelligence, and machine learning tasks, and protein similarity sequence search
アルゴリズム:Algorithms

Agent-Based Semantic Web Service Composition

Overview of Multi-Agent Systems A multi-agent system is a system in which multiple agents (AI characters...
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