Support Vector Machines

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
Stream Data Processing

Protected: Sequential learning using SVM

Overview of algorithms for sequential learning by adding/removing training examples using SVMs in support vector machines utilized for digital transformation, artificial intelligence, and machine learning tasks.
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.
アルゴリズム:Algorithms

Protected: Partitioning Method on Support Vector Machines (1) SMO Algorithm

Efficiency using the partitioning method (SMO algorithm) on support vector machines utilized for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks
アルゴリズム:Algorithms

Protected: kernel function

General kernel functions in support vector machines used in digital transformation (DX), artificial intelligence (AI), and machine learning, and kernel functions on probabilistic, string, and graph-type data
アルゴリズム:Algorithms

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)
アルゴリズム:Algorithms

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.
微分積分:Calculus

Protected: Regression Analysis with Support Vector Machines (1)Approach to linear regression problems

Regression problems with linear functions using dual problems with Lagrangian functions with support vector machines utilized in digital transformation, artificial intelligence, and machine learning tasks
機械学習:Machine Learning

Machine Learning Prootional Series – Support Vector Machines Reading Notes

Reading notes for a reference book on support vector machines (SVMs), a supervised learning pattern recognition model used for classification and regression in digital transformation, artificial intelligence, and machine learning tasks.
推論技術:inference Technology

Overview of Kernel Methods and Support Vector Machines

On kernel methods, one of the breakthroughs in machine learning technology
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