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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. The Fisher kernel was introduced in 1998.〔 Tommi Jaakkola and David Haussler (1998), Exploiting Generative Models in Discriminative Classifiers. In ''Advances in Neural Information Processing Systems 11'', pages 487–493. MIT Press. ISBN 978-0-262-11245-1 (PS ), (Citeseer )〕 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. == Derivation == 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Fisher kernel」の詳細全文を読む スポンサード リンク
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