Pattern recognition is a very active field of research intimately bound to machine learning.
This procedure, known as training, corresponds to learning an unknown decision function based only on a set of input-output pairs
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.
Many classifiers incorporate the general smoothness assumption that a test pattern similar to one of the training samples tends to be assigned to the same class.
The importance of prior knowledge in machine learning is suggested by its role in search and optimization.
Loosely, the no free lunch theorem states that all search algorithms have the same average performance over all problems, and thus implies that to gain in performance on a certain application one must use a specialized algorithm that includes some prior knowledge about the problem.
Another approach is to consider class-invariance with respect to a "domain of the input space" instead of a transformation.
A different type of class-invariance found in pattern recognition is 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 of the matrix inputs.
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: 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.