About: Fisher kernel

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In statistical classification, the Fisher kernel, named after 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.

Attributes	Values
rdf:type	yago:Ability105616246 yago:Abstraction100002137 yago:Cognition100023271 yago:Know-how105616786 yago:Method105660268 yago:PsychologicalFeature100023100 yago:WikicatKernelMethodsForMachineLearning disease
rdfs:label	Fisher kernel (en)
rdfs:comment	In statistical classification, the Fisher kernel, named after 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. (en)
dcterms:subject	Kernel methods for machine learning
Wikipage page ID	6855527 (xsd:integer)
Wikipage revision ID	1041229148 (xsd:integer)
Link from a Wikipage to another Wikipage	Ronald Fisher Kernel methods for machine learning Probabilistic latent semantic analysis Generative model Naive Bayes Feature (computer vision) Log-likelihood Bag-of-words model in computer vision Support vector machine Hidden Markov model Tf–idf Score (statistics) Similarity measure Statistical classification Fisher information Fisher information metric Bag of words model in computer vision
Link from a Wikipage to an external page	https://web.archive.org/web/20180627015707/https:/www.support-vector.net/
sameAs	Fisher kernel Fisher kernel Fisher kernel Fisher kernel
dbp:wikiPageUsesTemplate	dbt:ISBN
has abstract	In statistical classification, the Fisher kernel, named after 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. (en)
gold:hypernym	Function
prov:wasDerivedFrom	wikipedia-en:Fisher_kernel?oldid=1041229148&ns=0
page length (characters) of wiki page	5476 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Fisher_kernel
is Link from a Wikipage to another Wikipage of	Ronald Fisher Probabilistic latent semantic analysis Kernel method Bag-of-words model in computer vision Support vector machine List of statistics articles Outline of machine learning
is foaf:primaryTopic of	wikipedia-en:Fisher_kernel

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