Abstract
Supervised learning uses a labeled training set of typical objects. The learning refers to some form of adaptation of the classification algorithm to achieve a better response, which will help in classifying the unknown test set. Parametric methods use some form of probability distribution, while non-parametric methods use arbitrary distributions of unknown densities. Parametric learning relies on Bayesian decision theory, and can be easily linked to the concepts of discriminant functions and decision boundaries. [Non-parametric methods include artificial neural networks (ANNs) and support vector machines (SVMs).]
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Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York, NY (2001)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic, Amsterdam (2009)
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Dougherty, G. (2013). Supervised Learning. In: Pattern Recognition and Classification. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5323-9_5
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DOI: https://doi.org/10.1007/978-1-4614-5323-9_5
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