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- Fisher_kernel abstract "In statistical classification, the Fisher kernel, named in honour of Sir Ronald Fisher, is a function that measures the similarity of two objects on the basis of sets of measurements for each object and a statistical model. In a classification procedure, the class for a new object (whose real class is unknown) can be estimated by minimising, across classes, an average of the Fisher kernel distance from the new object to each known member of the given class.The Fisher kernel was introduced in 1998. It combines the advantages of generative statistical models (like the hidden Markov model) and those of discriminative methods (like support vector machines): generative models can process data of variable length (adding or removing data is well-supported) discriminative methods can have flexible criteria and yield better results.↑".
- Fisher_kernel wikiPageExternalLink www.support-vector.net.
- Fisher_kernel wikiPageID "6855527".
- Fisher_kernel wikiPageRevisionID "586857464".
- Fisher_kernel hasPhotoCollection Fisher_kernel.
- Fisher_kernel subject Category:Kernel_methods_for_machine_learning.
- Fisher_kernel comment "In statistical classification, the Fisher kernel, named in honour of Sir Ronald Fisher, is a function that measures the similarity of two objects on the basis of sets of measurements for each object and a statistical model.".
- Fisher_kernel label "Fisher kernel".
- Fisher_kernel sameAs m.025v02h.
- Fisher_kernel sameAs Q8563425.
- Fisher_kernel sameAs Q8563425.
- Fisher_kernel wasDerivedFrom Fisher_kernel?oldid=586857464.
- Fisher_kernel isPrimaryTopicOf Fisher_kernel.