Publication

Decision fusion using a multi-linear classifier

1998
Conference Papers

Abstract

The contribution of this paper is twofold: (1) to formulate a fusion problem encountered in the design of a multi-modal identity verification system as a particular classification problem, (2) to propose a simple classifier to solve this problem. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how much the claimed identity is verified. A fusion module receiving as input the d scores has to take a binary decision: accept or reject identity. We have formulated the design of this fusion module as a particular classification problem and developed a classifier based on a combination of half-spaces in the d-dimensional space to solve the problem. We call this classifier multi-linear classifier in reference to the use of several half-spaces, each one building a linear classifier. The performance of the fusion module has been evaluated on a multi-modal database.

Official source

https://infoscience.epfl.ch/entities/publication/34652a51-5454-47d6-a2d8-f6ee188bcfed

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1998
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/34652a51-5454-47d6-a2d8-f6ee188bcfed

About this result

Related concepts (32)

Statistical classification

In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.

Naive Bayes classifier

In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.

Linear classifier

In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector.

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Decision fusion using a multi-linear classifier

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