kernel methods
[/ˈkɜːrnəl ˈmɛθədz/]
nounpl: kernel methods
métodos de núcleo
1. A class of machine learning algorithms that use kernel functions to map input data into higher-dimensional spaces for classification, regression, and other learning tasks
Support Vector Machines are among the most popular kernel methods used in machine learning.
As Máquinas de Vetores de Suporte estão entre os métodos de núcleo mais populares utilizados em aprendizado de máquina.
2. Computational techniques that compute similarities between data points without explicitly transforming them into higher-dimensional feature spaces
Kernel methods allow us to work with non-linear decision boundaries efficiently.
Os métodos de núcleo nos permitem trabalhar com limites de decisão não-lineares de forma eficiente.
Kernel methods is primarily technical jargon used in machine learning and artificial intelligence fields. In Brazil, the term is typically used in its translated form 'métodos de núcleo' in academic contexts, though 'kernel methods' is frequently retained in technical documentation and research papers. This reflects the global nature of machine learning research and the prevalence of English in computational sciences. The concept is central to modern machine learning education and practice in both Brazil and the USA.
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