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Introduction to Machine Learning
Open accessAbstract
The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
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Metadata
- Authors
- Baştanlar, Y., & Özuysal, M.
- Venue
- Methods in Molecular Biology
- Publisher
- Humana Press
- Published
- 2013-11-11
- Volume
- 1107
- Pages
- 105-128
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- OpenAlex
- W3147809485
- Cited by
- 505
- References
- 2
- FWCI
- 6.55
- Type
- book chapter
- Language
- en
- Open access
- green
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- Baştanlar, Y., & Özuysal, M. (2014). Introduction to Machine Learning. Methods in Molecular Biology. https://doi.org/10.1007/978-1-62703-748-8_7
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