Dis- tances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These distances are combined into linear or nonlinear classifiers. In this approach the feature definition problem is replaced by finding good similarity measures.
Distances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These ...
In this paper the possibilities are discussed for training statistical pattern recognizers based on a distance representation of the objects instead of a ...
Distances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These ...
The featureless methodology is applied to the class of pattern recognition problems in which the adopted pairwise similarity measure possesses the most ...
Missing: classification. | Show results with:classification.
In this paper we discuss the possibility to construct classifiers entirely based on distances or similarities, without a relation with the feature space.
Missing: classification. | Show results with:classification.
A featureless approach to pattern recognition is an extension of classical machine learning techniques to the case when an object of recognition can be ...
Featureless Pattern Recognition in an Imaginary Hilbert Space and Its ...
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Jul 26, 2001 · The featureless pattern recognition methodology based on measuring some numerical characteristics of similarity between pairs of entities is ...
The featureless pattern recognition methodology based on measuring some numerical characteristics of similarity between pairs of entities is applied to the ...
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The featureless pattern recognition methodology based on measuring some numerical characteristics of similarity between pairs of entities is applied to the ...