機械学習:Machine Learning

アルゴリズム:Algorithms

Protected: Calculation of marginal probability distributions – Probability Propagation Method

Compute the probability distribution around graphical models in probabilistic generative models used in digital transformation, artificial intelligence , and machine learning tasks, such as Bayesian estimation, using probability propagation methods
アルゴリズム:Algorithms

Various probability distributions

Overview of various probabilistic models used as approximate models for probabilistic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (Student's t distribution, Wishart distribution, Gaussian distribution, gamma distribution, inverse gamma distribution, Dirichlet distribution, beta distribution, categorical distribution, Poisson distribution, Bernoulli distribution)
アルゴリズム:Algorithms

Protected: Overview of Gaussian Processes(2)Gaussian Processes and Kernels

Gaussian processes and kernel functions (Mattern kernel, character kernel, Fisher kernel, HMM's marginalized kernel, linear kernel, exponential kernel, periodic kernel, RBF kernel), which are dimensionless stochastic generative models used for digital transformation, artificial intelligence and machine learning tasks
Stream Data Processing

Reasoning Web2020 Papers

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
Symbolic Logic

Reasoning Web 2019 Papers

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
アルゴリズム:Algorithms

Protected: Partitioning Methods in Support Vector Machines (2) DCDM Algorithm for Linear SVM

DCDM algorithm (dual coordinate descent method algorithm), an efficient algorithm for processing large amounts of (sparse) data on support vector machines (algorithm for linear SVM used in LIBLINEAR) used in digital transformation (DX), artificial intelligence (AI) and machine learning (ML) tasks.
IOT技術:IOT Technology

Protected: Applications of State Space Models to Marketing

Application to marketing using evolution and evolution in time-series data analysis using state-space models utilized in digital transformation, artificial intelligence, and machine learning tasks.
アルゴリズム:Algorithms

Protected: Graphical model with factor graph representation

An overview of factor graph models, a more generalized version of graphical models used in probabilistic generative models for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks
アルゴリズム:Algorithms

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
アルゴリズム:Algorithms

Protected: Overview of Gaussian Processes (1) Gaussian Processes and Kernel Tricks

Overview of the theory of Gaussian processes (support vector machines, related vector machines, RVMs, RBF kernels, kernel functions), which are dimensionless multivariate Gaussian distribution models with no specified parameters among stochastic generative models used in digital transformation, artificial intelligenceand machine learningtasks.
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