Prior knowledge for pattern recognition: Difference between revisions

Revision as of 14:01, 16 January 2012

Revision as of 06:17, 7 February 2012

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{{Orphan|date=November 2006}}

[[Pattern recognition]] is a very active field of research intimately bound to [[machine learning]]. Also known as classification or [[statistical classification]], pattern recognition aims at building a [[classifier]] that can determine the class of an input pattern. This procedure, known as training, corresponds to learning an unknown decision function based only on a set of input-output pairs <math>(\boldsymbol{x}_i,y_i)</math> that form the training data (or training set). Nonetheless, in real world applications such as [[character recognition]], a certain amount of information on the problem is usually known beforehand. The incorporation of this prior knowledge into the training is the key element that will allow an increase of performance in many applications.

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A very common type of prior knowledge in pattern recognition is the invariance of the class (or the output of the classifier) to a [[Transformation (geometry)|transformation]] of the input pattern. This type of knowledge is referred to as '''transformation-invariance'''. The mostly used transformations used in image recognition are:

* [[Translation_Translation (geometry)|translation]];

* [[Rotation_Rotation (mathematics)|rotation]];

* [[skewing]];

* [[Scaling_Scaling (geometry)|scaling]].

Incorporating the invariance to a transformation <math>T_{\theta}: \boldsymbol{x} \mapsto T_{\theta}\boldsymbol{x}</math> parametrized in <math>\theta</math> into a classifier of output <math>f(\boldsymbol{x})</math> for an input pattern <math>\boldsymbol{x}</math> corresponds to enforcing the equality

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* '''Unlabeled samples''' are available with supposed class-memberships;

* '''Imbalance''' of the training set due to a high proportion of samples of a class;

* '''Quality of the data''' may vary from a sample to another.

Prior knowledge of these can enhance the quality of the recognition if included in the learning. Moreover, not taking into account the poor quality of some data or a large imbalance between the classes can mislead the decision of a classifier.

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<references/>

== References ==