Abstract
Machine Learning is one of the key areas of Artificial Intelligence and it concerns the study and the development of quantitative models that enables a computer to perform tasks without being explicitly programmed to do them. Learning in this context is hence to recognize complex forms and to make intelligent decisions. Given all existing entries, the difficulty of this task lies in the fact that all possible decisions is usually very complex to enumerate. To get around that, machine learning algorithms are designed in order to gain knowledge on the problem to be addressed based on a limited set of observed data extracted from this problem.
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© 2015 Springer International Publishing Switzerland
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Amini, MR., Usunier, N. (2015). Introduction. In: Learning with Partially Labeled and Interdependent Data. Springer, Cham. https://doi.org/10.1007/978-3-319-15726-9_1
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DOI: https://doi.org/10.1007/978-3-319-15726-9_1
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Online ISBN: 978-3-319-15726-9
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