Abstract
Classification assigns objects to various classes based on measured features. The features are considered as a feature vector in feature space. It is important to select the most informative features and/or combine features for successful classification. Typically a sample set (the training set) is selected to train the classifier, which is then applied to other objects (the test set). Supervised learning uses a labeled training set, in which it is known to which class the objects belong, and is an inductive reasoning process. There are a variety of approaches to classification; statistical approaches, characterized by an underlying probability model, are very important. We will consider a number of robust features and examples based on shape, size, and topology to classify various objects.
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Dougherty, G. (2013). Classification. In: Pattern Recognition and Classification. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5323-9_2
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DOI: https://doi.org/10.1007/978-1-4614-5323-9_2
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