Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/5770
Title: | Introduction to machine learning | Authors: | Baştanlar, Yalın Özuysal, Mustafa |
Keywords: | Machine learning Dimensionality reduction Classification Clustering Performance metrics Regression |
Publisher: | Humana Press | Source: | Baştanlar, Y., and Özuysal, M. (2014). Introduction to machine learning. Methods in Molecular Biology, 1107, 105-128. doi:10.1007/978-1-62703-748-8_7 | Abstract: | 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. | URI: | http://doi.org/10.1007/978-1-62703-748-8_7 http://hdl.handle.net/11147/5770 |
ISSN: | 1940-6029 1064-3745 |
Appears in Collections: | Computer Engineering / Bilgisayar Mühendisliği PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Show full item record
CORE Recommender
SCOPUSTM
Citations
322
checked on Nov 15, 2024
WEB OF SCIENCETM
Citations
242
checked on Nov 9, 2024
Page view(s)
75,444
checked on Nov 18, 2024
Download(s)
72,800
checked on Nov 18, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.