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Fisher kernel, the Glossary

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.^[1]

15 relations: Bag-of-words model in computer vision, Feature (computer vision), Fisher information, Fisher information metric, Generative model, Hidden Markov model, Informant (statistics), Likelihood function, Naive Bayes classifier, Probabilistic latent semantic analysis, Ronald Fisher, Similarity measure, Statistical classification, Support vector machine, Tf–idf.
Kernel methods for machine learning

Bag-of-words model in computer vision

In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model can be applied to image classification or retrieval, by treating image features as words.

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Feature (computer vision)

In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties.

See Fisher kernel and Feature (computer vision)

Fisher information

In mathematical statistics, the Fisher information (sometimes simply called information) is a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ of a distribution that models X. Formally, it is the variance of the score, or the expected value of the observed information.

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Fisher information metric

In information geometry, the Fisher information metric is a particular Riemannian metric which can be defined on a smooth statistical manifold, i.e., a smooth manifold whose points are probability measures defined on a common probability space.

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Generative model

In statistical classification, two main approaches are called the generative approach and the discriminative approach.

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A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as X). An HMM requires that there be an observable process Y whose outcomes depend on the outcomes of X in a known way.

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Informant (statistics)

In statistics, the score (or informant) is the gradient of the log-likelihood function with respect to the parameter vector.

See Fisher kernel and Informant (statistics)

Likelihood function

A likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model.

See Fisher kernel and Likelihood function

Naive Bayes classifier

In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class.

See Fisher kernel and Naive Bayes classifier

Probabilistic latent semantic analysis

Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles) is a statistical technique for the analysis of two-mode and co-occurrence data.

See Fisher kernel and Probabilistic latent semantic analysis

Ronald Fisher

Sir Ronald Aylmer Fisher (17 February 1890 – 29 July 1962) was a British polymath who was active as a mathematician, statistician, biologist, geneticist, and academic.

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Similarity measure

In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects.

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Statistical classification

When classification is performed by a computer, statistical methods are normally used to develop the algorithm.

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Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.

See Fisher kernel and Support vector machine

Tf–idf

In information retrieval, tf–idf (also TF*IDF, TFIDF, TF–IDF, or Tf–idf), short for term frequency–inverse document frequency, is a measure of importance of a word to a document in a collection or corpus, adjusted for the fact that some words appear more frequently in general.

See Fisher kernel and Tf–idf

References

[1] https://en.wikipedia.org/wiki/Fisher_kernel