Abstract
In machine learning we distinguish various approaches between two extreme ones: unsupervised and supervised learning. The task of unsupervised learning consists in grouping similar data points in the description space thus inducing a structure on it. Then the data model can be expressed in terms of space partition. Probably, the most popular of such grouping algorithms in visual content mining is the K-means approach introduced by MacQueen as early as in 1967, at least this is the approach which was used for the very popular Bag-of-Visual Words model we have mentioned in Chap. 1. The Deep learning approach is a part of the family of supervised learning methods designed both for classification and regression. In this very short chapter we will focus on the formal definition of supervised learning approach, but also on fundamentals of evaluation of classification algorithms as the evaluation metrics will be used further in the book.
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Zemmari, A., Benois-Pineau, J. (2020). Supervised Learning Problem Formulation. In: Deep Learning in Mining of Visual Content. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-34376-7_2
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DOI: https://doi.org/10.1007/978-3-030-34376-7_2
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