Keywords

#support_vector_machine

Support vector machine

Set of methods for supervised statistical learning

In machine learning, support vector machines are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik and Chervonenkis (1974).

Mon 26th

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Support vector machine Structured support vector machine Relevance vector machine Least-squares support vector machine Elastic net regularization Kernel method Supervised learning Vladimir Vapnik Vector database Machine learning
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