Fisher Kernel Function
A Fisher Kernel Function is a kernel function that measures the similarity of two objects on the basis of sets of measurements for each object and a statistical model.
- See: Fisher Score, Fisher Discriminant Analysis, Statistical Classification, Support Vector Machines, Generative Model.
References
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Fisher_kernel Retrieved:2014-9-29.
- 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. [1] 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.
- 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.