In 1971, Kimeldorf proposed a method of constructing kernel space based on support vectors. In 1990s, V.Vapnik formally introduced the Support Vector Machine (SVM) methods in Statistical Learning.

Support Vector Machine is a versatile and powerful algorithm for classification and regression tasks. Its ability to handle high-dimensional data, its robustness to outliers, and its ability to learn ...

Support Vector Machines (SVMs in short) are machine learning algorithms that are used for classification and regression purposes. SVMs are one of the powerful machine learning algorithms for ...

Expert systems contribute to optimal parameter selection for Support Vector Machines by: Incorporating domain-specific knowledge to guide parameter choices. Automating the tuning process, reducing ...

Support Vector Machines are a powerful class of supervised machine-learning algorithms used for classification and regression tasks. They are particularly effective when dealing with complex datasets.

By applying the method of support vector machine, we carry on a classification study of existing diagram data of the Chinese herbal medicine fingerprint. We compared the effect of support vector ...

Therefore, stainless steel is a difficult-to-machine metal material [1]. When turning 304 stainless steel, Support vector machine (SVM) and Particle swarm algorithm (PSO) are used to optimize the ...