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Mašinsko učenje[2] je podoblast umjetne inteligencije čiji je cilj stvaranje algoritama i računarskih sistema koji su sposobni da se prilagode novim analognim situacijama i uče na osnovu iskustva. Razvijene su različite tehnike učenja za obavljanje različitih zadataka. Prve koje su bile predmet istraživanja odnose se na nadgledano učenje za diskreciono donošenje odluka, nadgledano učenje za kontinuirano predviđanje i pojačano učenje za sekvencijalno donošenje odluka, kao i učenje bez nadzora.
Do sada je od svih navedenih zadataka najbolje shvaćeno odlučivanje kroz jedan pokušaj (engleski: one-shot learning). Računaru se daje opis jednog objekta (događaja ili situacije) i očekuje se da kao rezultat ispiše klasifikaciju tog objekta. Na primjer, program za prepoznavanje alfanumeričkih znakova uzima kao ulaz digitaliziranu sliku nekog alfanumeričkog znaka i kao izlaz bi trebao ispisati njegovo ime.
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- Sergios Theodoridis, Konstantinos Koutroumbas (2009) "Pattern Recognition", 4th Edition, Academic Press. ISBN 978-1-59749-272-0
- Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 978-0-262-01211-9
- Bing Liu (2007), Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, ISBN 978-3-540-37881-5
- Toby Segaran, Programming Collective Intelligence, O'Reilly ISBN 978-0-596-52932-1
- Ray Solomonoff, "An Inductive Inference Machine" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI
- Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56-62, 1957
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- Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 978-0-19-853864-6
- Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 978-0-471-05669-0
- Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 978-3-540-31681-7
- KECMAN Vojislav (2001), Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 978-0-262-11255-0
- MacKay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 978-0-521-64298-9
- Ian H. Witten and Eibe Frank Data Mining: Practical machine learning tools and techniques Morgan Kaufmann ISBN 978-0-12-088407-0
- Sholom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 978-1-55860-065-2
- Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006
- Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 978-0-387-95284-0
- Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 978-0-471-03003-4
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