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Subject Item
dbr:Pattern_recognition
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dbr:Prior_knowledge_for_pattern_recognition
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dbr:Prior_(disambiguation)
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Prior knowledge for pattern recognition
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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 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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dbc:Machine_learning dbc:Statistical_classification
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dbr:Transformation_(geometry) dbr:Translation_(geometry) dbr:Character_recognition dbr:Classifier_(mathematics) dbr:Pattern_recognition dbr:Rotation_(mathematics) dbr:Ill-posed dbr:Skewing dbr:Model_(abstract) dbc:Statistical_classification dbr:No_free_lunch_in_search_and_optimization dbr:Machine_learning dbr:Scaling_(geometry) dbc:Machine_learning dbr:Statistical_classification
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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 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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