Prior knowledge for pattern recognition: Difference between revisions

Revision as of 09:32, 7 October 2010

Revision as of 04:58, 29 September 2011

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It must be noted that <math>f</math> in these Equationsequations can be either the decision function of the classifier or its real-valued output.

Another approach is to consider the class-invariance with respect to a "___domain of the input space" instead of a transformation. In this case, the problem becomes finding <math>f</math> so that

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A different type of class-invariance found in pattern recognition is the '''permutation-invariance''', i.e. invariance of the class to a permutation of elements in a structured input. A typical application of this type of prior knowledge is a classifier invariant to permutations of rows in matrix inputs.

== Knowledge onof the data ==

Other forms of prior knowledge than class-invariance concern the data more specifically and are thus of particular interest for real-world applications. The three particular cases that most often occur when gathering data are:

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

Prior knowledge onof 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.

== Notes ==