Машинно самообучение (Bulgarian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Машинно самообучение" in Bulgarian language version.

refsWebsite

Global rank Bulgarian rank

11doi.org

2^nd place

7^th place

5archive.org

6^th place

11^th place

3web.archive.org

1^st place

2springer.com

274^th place

579^th place

2books.google.com

3^rd place

10^th place

2athenasc.com

low place

2deepai.org

low place

1soton.ac.uk

6,219^th place

low place

1reddit.com

1,082^nd place

4,554^th place

1projecteuclid.org

3,707^th place

7,114^th place

1usc.edu

1,077^th place

1,021^st place

1mit.edu

415^th place

414^th place

1wustl.edu

3,160^th place

low place

1cmu.edu

1,564^th place

3,533^rd place

1utoronto.ca

1,601^st place

1,701^st place

1plantcell.org

low place

1hindawi.com

6,828^th place

low place

1ed.ac.uk

1,871^st place

6,247^th place

1yale.edu

565^th place

476^th place

1acm.org

1,185^th place

1,746^th place

1frontiersin.org

4,679^th place

low place

1psu.edu

207^th place

1,406^th place

1ccas.ru

low place

acm.org

dl.acm.org

Shapiro, Ehud Y. "The model inference system." Proceedings of the 7th international joint conference on Artificial intelligence-Volume 2. Morgan Kaufmann Publishers Inc., 1981.

archive.org

Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006. ISBN 0-387-31073-8.
Mitchell, T. Machine Learning. McGraw Hill, 1997. ISBN 978-0-07-042807-2. с. 2.
Russell, Stuart J., Norvig, Peter. Artificial Intelligence: A Modern Approach. Third. Prentice Hall, 2010. ISBN 9780136042594.
Neural Networks // Computer Science Handbook, Second Edition (Section VII: Intelligent Systems). Boca Raton, Florida, Chapman & Hall/CRC Press LLC, 2004. ISBN 1-58488-360-X.
Michie, D. и др. Machine Learning, Neural and Statistical Classification // Ellis Horwood Series in Artificial Intelligence. 1994.

athenasc.com

Dimitri P. Bertsekas. „Dynamic Programming and Optimal Control: Approximate Dynamic Programming, Vol.II“, Athena Scientific, 2012,[1]
Dimitri P. Bertsekas and John N. Tsitsiklis. „Neuro-Dynamic Programming“, Athena Scientific, 1996,[2]

books.google.com

Alpaydin, Ethem. Introduction to Machine Learning. MIT Press, 2010. ISBN 978-0-262-01243-0. с. 9.
Yoshua Bengio. Learning Deep Architectures for AI. Now Publishers Inc., 2009. ISBN 978-1-60198-294-0. с. 1 – 3.

ccas.ru

К. В. Воронцов. Лекции по методу опорных векторов

cmu.edu

cs.cmu.edu

Visual categorization with bags of keypoints // ECCV Workshop on Statistical Learning in Computer Vision. 2004.

deepai.org

How Does Association Learning Work? // deepai.org.
Build with AI | DeepAI // DeepAI. Посетен на 6 октомври 2018.

doi.org

dx.doi.org

Samuel, Arthur. Some Studies in Machine Learning Using the Game of Checkers // IBM Journal of Research and Development 3 (3). 1959. DOI:10.1147/rd.33.0210. с. 210 – 229.
Определението „без да са изрично програмирани“ често се приписва на Артър Самюел, който въвежда термина machine learning през 1959 г., но фразата не се среща в този вид в споменатото издание и може би е парафраза, появила се по-късно. Вижте „Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?“ в Koza, John R. Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming // Artificial Intelligence in Design '96. Springer, Dordrecht, 1996, 151 – 170 pp. DOI:10.1007/978-94-009-0279-4_9. (на английски)
Langley, Pat. The changing science of machine learning // Machine Learning 82 (3). 2011. DOI:10.1007/s10994-011-5242-y. с. 275 – 279.
Representation Learning: A Review and New Perspectives // IEEE Trans. PAMI, special issue Learning Deep Architectures 35. 2013. DOI:10.1109/tpami.2013.50. с. 1798 – 1828.
Lu, Haiping и др. A Survey of Multilinear Subspace Learning for Tensor Data // Pattern Recognition 44. 2011. DOI:10.1016/j.patcog.2011.01.004. с. 1540 – 1551. Архивиран от оригинала на 2019-07-10.
Tillmann, A. M. On the Computational Intractability of Exact and Approximate Dictionary Learning // IEEE Signal Processing Letters 22 (1). 2015. DOI:10.1109/LSP.2014.2345761. с. 45 – 49.
Bassel, George W. et al. Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets // The Plant Cell 23 (9). 1 септември 2011. DOI:10.1105/tpc.111.088153. p. 3101 – 3116. (на английски)
Mining association rules between sets of items in large databases // Proceedings of the 1993 ACM SIGMOD international conference on Management of data – SIGMOD '93. 1993. ISBN 0897915925. DOI:10.1145/170035.170072. с. 207.
Urbanowicz, Ryan J. et al. Learning Classifier Systems: A Complete Introduction, Review, and Roadmap // Journal of Artificial Evolution and Applications 2009. 22 септември 2009. DOI:10.1155/2009/736398. p. 1 – 25. (на английски)
Cortes, Corinna и др. Support-vector networks // Machine Learning 20 (3). 1995. DOI:10.1007/BF00994018. с. 273 – 297.
Goldberg, David E. и др. Genetic algorithms and machine learning // Machine Learning 3 (2). 1988. DOI:10.1007/bf00113892. с. 95 – 99.

ed.ac.uk

era.lib.ed.ac.uk

Plotkin G.D. Automatic Methods of Inductive Inference, PhD thesis, University of Edinburgh, 1970.

frontiersin.org

Artificial Neural Networks as Models of Neural Information Processing | Frontiers Research Topic // Посетен на 20 февруари 2018. (на английски)

hindawi.com

Urbanowicz, Ryan J. et al. Learning Classifier Systems: A Complete Introduction, Review, and Roadmap // Journal of Artificial Evolution and Applications 2009. 22 септември 2009. DOI:10.1155/2009/736398. p. 1 – 25. (на английски)

mit.edu

mitpress.mit.edu

Alpaydin, Ethem. Introduction to Machine Learning. London, The MIT Press, 2010. ISBN 978-0-262-01243-0. Посетен на 4 февруари 2017.

plantcell.org

Bassel, George W. et al. Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets // The Plant Cell 23 (9). 1 септември 2011. DOI:10.1105/tpc.111.088153. p. 3101 – 3116. (на английски)

projecteuclid.org

Cornell University Library. Breiman: Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) // Посетен на 8 август 2015.

psu.edu

citeseerx.ist.psu.edu

Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng. "Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations" Proceedings of the 26th Annual International Conference on Machine Learning, 2009.

reddit.com

Michael I. Jordan. statistics and machine learning // reddit, 10 септември 2014. Посетен на 1 октомври 2014.

soton.ac.uk

eprints.ecs.soton.ac.uk

Harnad, Stevan. The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence // The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Kluwer, 2008.

springer.com

link.springer.com

Определението „без да са изрично програмирани“ често се приписва на Артър Самюел, който въвежда термина machine learning през 1959 г., но фразата не се среща в този вид в споменатото издание и може би е парафраза, появила се по-късно. Вижте „Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?“ в Koza, John R. Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming // Artificial Intelligence in Design '96. Springer, Dordrecht, 1996, 151 – 170 pp. DOI:10.1007/978-94-009-0279-4_9. (на английски)
Reinforcement learning and markov decision processes // Reinforcement Learning. Springer Berlin Heidelberg, 2012. с. 3 – 42.

usc.edu

www-bcf.usc.edu

An Introduction to Statistical Learning. Springer, 2013. с. vii. Архив на оригинала от 2019-06-23 в Wayback Machine.

utoronto.ca

dsp.utoronto.ca

Lu, Haiping и др. A Survey of Multilinear Subspace Learning for Tensor Data // Pattern Recognition 44. 2011. DOI:10.1016/j.patcog.2011.01.004. с. 1540 – 1551. Архивиран от оригинала на 2019-07-10.

web.archive.org

An Introduction to Statistical Learning. Springer, 2013. с. vii. Архив на оригинала от 2019-06-23 в Wayback Machine.
An analysis of single-layer networks in unsupervised feature learning // Int'l Conf. on AI and Statistics (AISTATS). 2011. Архив на оригинала от 2017-08-13 в Wayback Machine.
Lu, Haiping и др. A Survey of Multilinear Subspace Learning for Tensor Data // Pattern Recognition 44. 2011. DOI:10.1016/j.patcog.2011.01.004. с. 1540 – 1551. Архивиран от оригинала на 2019-07-10.

wustl.edu

machinelearning.wustl.edu

An analysis of single-layer networks in unsupervised feature learning // Int'l Conf. on AI and Statistics (AISTATS). 2011. Архив на оригинала от 2017-08-13 в Wayback Machine.

yale.edu

ftp.cs.yale.edu

Shapiro, Ehud Y. Inductive inference of theories from facts, Research Report 192, Yale University, Department of Computer Science, 1981. Reprinted in J.-L. Lassez, G. Plotkin (Eds.), Computational Logic, The MIT Press, Cambridge, MA, 1991, pp. 199 – 254.