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Support Vector Machines (SVM)

Supervised machine learning models used for classification and regression.

SVM

Support Vector Machines (SVMs) are supervised machine learning models used for classification and regression. They find the optimal hyperplane that maximizes the margin between different classes in a dataset, aiming to effectively separate data points while minimizing misclassifications. SVMs can also utilize kernel functions to map data into higher dimensions, enabling non-linear decision boundaries.

Etymology

Support Vector Machines (SVM) originated from statistical learning theory in the 1990s.

Explain Like I’m 5

Support Vector Machines (SVMs) are like drawing lines to separate different groups of things, helping computers learn to tell them apart.

Fun Facts

SVMs were initially developed for pattern recognition
They can be effective even with high-dimensional data
SVMs use a 'kernel trick' to handle complex relationships

FAQ

What is SVM? SVMs are supervised machine learning models
What can SVMs do? They can be used for classification and regression
Who uses SVMs? Researchers and developers use them in various applications

Hypernyms — Broader Categories

supervised machine learning

Holonyms — Wholes or Groups

machine learning

Indexes

classification model machine learning algorithm

Related

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