Abstract: Abstract Pattern recognition is about assigning objects (also called observations , instances , or examples ) to classes. The objects are described by features and represented as points in the feature space. A classifier is an algorithm that assigns a class label to any given point in the feature space. Pattern recognition comprises supervised learning (predefined class labels) and unsupervised learning (unknown class labels). Supervised learning includes choosing a classifier model, training and testing the classifier, and selecting the relevant features. Classifier ensembles combine the outputs of a set of classifiers for improved accuracy. Unsupervised learning is usually approached by cluster analysis.
Publication Year: 2015
Publication Date: 2015-03-31
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 10
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