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- Cover's_theorem abstract "Cover's Theorem is a statement in computational learning theory and is one of the primary theoretical motivations for the use of non-linear kernel methods in machine learning applications. The theorem states that given a set of training data that is not linearly separable, one can with high probability transform it into a training set that is linearly separable by projecting it into a higher-dimensional space via some non-linear transformation.The proof is easy. A deterministic mapping may be used. Indeed, suppose there are samples. Lift them onto the vertices of the simplex in the dimensional real space. Every partition of the samples into two sets is separable by a linear separator. QED. A complex pattern-classification problem, cast in a high-dimensional space nonlinearly, is more likely to be linearly separable than in a low-dimensional space, provided that the space is not densely populated. — Cover, T.M. , Geometrical and Statistical properties of systems of linear inequalities with applications in pattern recognition., 1965".
- Cover's_theorem wikiPageExternalLink v=onepage&q=Cover's%20theorem&f=false.
- Cover's_theorem wikiPageID "24364159".
- Cover's_theorem wikiPageRevisionID "603813718".
- Cover's_theorem hasPhotoCollection Cover's_theorem.
- Cover's_theorem subject Category:Computational_learning_theory.
- Cover's_theorem subject Category:Neural_networks.
- Cover's_theorem subject Category:Statistical_classification.
- Cover's_theorem type Abstraction100002137.
- Cover's_theorem type Communication100033020.
- Cover's_theorem type ComputerArchitecture106725249.
- Cover's_theorem type Description106724763.
- Cover's_theorem type Message106598915.
- Cover's_theorem type NeuralNetwork106725467.
- Cover's_theorem type NeuralNetworks.
- Cover's_theorem type Specification106725067.
- Cover's_theorem type Statement106722453.
- Cover's_theorem comment "Cover's Theorem is a statement in computational learning theory and is one of the primary theoretical motivations for the use of non-linear kernel methods in machine learning applications. The theorem states that given a set of training data that is not linearly separable, one can with high probability transform it into a training set that is linearly separable by projecting it into a higher-dimensional space via some non-linear transformation.The proof is easy.".
- Cover's_theorem label "Cover's theorem".
- Cover's_theorem sameAs m.07s5vnf.
- Cover's_theorem sameAs Q5179139.
- Cover's_theorem sameAs Q5179139.
- Cover's_theorem sameAs Cover's_theorem.
- Cover's_theorem wasDerivedFrom Cover's_theorem?oldid=603813718.
- Cover's_theorem isPrimaryTopicOf Cover's_theorem.