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- Relevance_vector_machine abstract "In mathematics, a relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification.The RVM has an identical functional form to the support vector machine, but provides probabilistic classification.It is actually equivalent to a Gaussian process model with covariance function:where is the kernel function (usually Gaussian), and are the input vectors of the training set.[citation needed]Compared to that of support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM (that usually require cross-validation-based post-optimizations). However RVMs use an expectation maximization (EM)-like learning method and are therefore at risk of local minima. This is unlike the standard sequential minimal optimization (SMO)-based algorithms employed by SVMs, which are guaranteed to find a global optimum (of the convex problem).The relevance vector machine is patented in the United States by Microsoft.".
- Relevance_vector_machine wikiPageExternalLink dlib.net.
- Relevance_vector_machine wikiPageExternalLink index.html.
- Relevance_vector_machine wikiPageExternalLink www.relevancevector.com.
- Relevance_vector_machine wikiPageExternalLink kml.
- Relevance_vector_machine wikiPageExternalLink RVM%20Explained.pdf.
- Relevance_vector_machine wikiPageID "4195092".
- Relevance_vector_machine wikiPageRevisionID "602399427".
- Relevance_vector_machine hasPhotoCollection Relevance_vector_machine.
- Relevance_vector_machine subject Category:Classification_algorithms.
- Relevance_vector_machine subject Category:Kernel_methods_for_machine_learning.
- Relevance_vector_machine subject Category:Non-parametric_Bayesian_methods.
- Relevance_vector_machine type Ability105616246.
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- Relevance_vector_machine type Act100030358.
- Relevance_vector_machine type Activity100407535.
- Relevance_vector_machine type Algorithm105847438.
- Relevance_vector_machine type ClassificationAlgorithms.
- Relevance_vector_machine type Cognition100023271.
- Relevance_vector_machine type Event100029378.
- Relevance_vector_machine type Know-how105616786.
- Relevance_vector_machine type Method105660268.
- Relevance_vector_machine type Non-parametricBayesianMethods.
- Relevance_vector_machine type Procedure101023820.
- Relevance_vector_machine type PsychologicalFeature100023100.
- Relevance_vector_machine type Rule105846932.
- Relevance_vector_machine type YagoPermanentlyLocatedEntity.
- Relevance_vector_machine comment "In mathematics, a relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification.The RVM has an identical functional form to the support vector machine, but provides probabilistic classification.It is actually equivalent to a Gaussian process model with covariance function:where is the kernel function (usually Gaussian), and are the input vectors of the training set.[citation needed]Compared to that of support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM (that usually require cross-validation-based post-optimizations). ".
- Relevance_vector_machine label "Relevance vector machine".
- Relevance_vector_machine label "相关向量机".
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