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Machine Learning and Data Mining in Bioinformatics

Machine Learning and Data Mining in Bioinformatics
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Author(s): George Tzanis (Aristotle University of Thessaloniki, Greece), Christos Berberidis (Aristotle University of Thessaloniki, Greece)and Ioannis Vlahavas (Aristotle University of Thessaloniki, Greece)
Copyright: 2009
Pages: 10
Source title: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
Source Author(s)/Editor(s): Viviana E. Ferraggine (UNICEN, Argentina), Jorge Horacio Doorn (UNICEN, Argentina)and Laura C. Rivero (UNICEN, Argentina)
DOI: 10.4018/978-1-60566-242-8.ch066

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Abstract

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

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