Machine learning
Machine learning gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1][2] It is a subfield of computer science.
The idea came from work in artificial intelligence.[3] Machine learning explores the study and construction of algorithms which can learn and make predictions on data.[4] Such algorithms follow programmed instructions, but can also make predictions or decisions based on data.[5]: 2 They build a model from sample inputs.
Machine learning is done where designing and programming explicit algorithms cannot be done. Examples include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[6] optical character recognition (OCR),[7] search engines and computer vision.
References
- ↑ John McCarthy & Edward Feigenbaum 1990. In Memoriam Arthur Samuel: pioneer in machine learning. AI Magazine. AAAI. 11 (3).[1]
- ↑ Phil Simon (2013). Too big to ignore: the business case for big data. Wiley. p. 89. ISBN 978-1-118-63817-0.
- ↑ [2]
- ↑ Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274.
- ↑ Christopher Bishop 1995. Neural networks for pattern recognition. Oxford University Press. ISBN 0-19-853864-2
- ↑ "TechCrunch".
- ↑ Wernick et al 2010. Machine learning in medical imaging, IEEE Signal Processing Society|IEEE Signal Processing Magazine. 27, 4, 25-38.