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Machine Learning and Data Mining in Bioinformatics

Machine Learning and Data Mining in Bioinformatics

George Tzanis, Christos Berberidis, Ioannis Vlahavas
Copyright: © 2012 |Pages: 9
ISBN13: 9781609608187|ISBN10: 1609608186|EISBN13: 9781609608194
DOI: 10.4018/978-1-60960-818-7.ch401
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MLA

Tzanis, George, et al. "Machine Learning and Data Mining in Bioinformatics." Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 695-703. https://doi.org/10.4018/978-1-60960-818-7.ch401

APA

Tzanis, G., Berberidis, C., & Vlahavas, I. (2012). Machine Learning and Data Mining in Bioinformatics. In I. Management Association (Ed.), Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 695-703). IGI Global. https://doi.org/10.4018/978-1-60960-818-7.ch401

Chicago

Tzanis, George, Christos Berberidis, and Ioannis Vlahavas. "Machine Learning and Data Mining in Bioinformatics." In Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 695-703. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-818-7.ch401

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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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