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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 1/2013

01.03.2013 | Review

A survey of methods for distributed machine learning

verfasst von: Diego Peteiro-Barral, Bertha Guijarro-Berdiñas

Erschienen in: Progress in Artificial Intelligence | Ausgabe 1/2013

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Abstract

Traditionally, a bottleneck preventing the development of more intelligent systems was the limited amount of data available. Nowadays, the total amount of information is almost incalculable and automatic data analyzers are even more needed. However, the limiting factor is the inability of learning algorithms to use all the data to learn within a reasonable time. In order to handle this problem, a new field in machine learning has emerged: large-scale learning. In this context, distributed learning seems to be a promising line of research since allocating the learning process among several workstations is a natural way of scaling up learning algorithms. Moreover, it allows to deal with data sets that are naturally distributed, a frequent situation in many real applications. This study provides some background regarding the advantages of distributed environments as well as an overview of distributed learning for dealing with “very large” data sets.
Nächster Artikel Learning domain structure through probabilistic policy reuse in reinforcement learning

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Literatur
1.
2.
3.
Catlett, J.: Megainduction: machine learning on very large databases. PhD thesis, School of Computer Science, University of Technology, Sydney, Australia (1991)
4.
Bottou, L., Bousquet, O.: The tradeoffs of large scale learning. Adv. Neural Inf. Process. Syst. 20, 161–168 (2008)
5.
Sonnenburg, S., Ratsch, G., Rieck, K.: Large scale learning with string kernels. In: Bottou, L., Chapelle, O., DeCoste, D., Weston, J. (eds.) Large Scale Kernel Machines, pp. 73–104. MIT Press, Cambridge (2007)
6.
Moretti, C., Steinhaeuser, K., Thain, D., Chawla, N.V.: Scaling up classifiers to cloud computers. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 472–481 (2008)
7.
Krishnan, S., Bhattacharyya, C., Hariharan, R.: A randomized algorithm for large scale support vector learning. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 793–800 (2008)
8.
Raina, R., Madhavan, A., Ng., A.Y.: Large-scale deep unsupervised learning using graphics processors. In: Proceedings of the 26th Annual International Conference on Machine Learning (ICML), pp. 873–880 (2009)
9.
Provost, F., Kolluri, V.: A survey of methods for scaling up inductive algorithms. Data Min. Knowl. Discov. 3(2), 131–169 (1999)CrossRef
10.
Giordana, A., Neri, F.: Search-intensive concept induction. Evol. Comput. 3(4), 375–416 (1995)CrossRef
11.
Anglano, C., Botta, M.: Now g-net: learning classification programs on networks of workstations. IEEE Trans. Evol. Comput. 6(5), 463–480 (2002)CrossRef
12.
Rodríguez, M., Escalante, D.M., Peregrín, A.: Efficient distributed genetic algorithm for rule extraction. Appl. Soft Comput. 11(1), 733–743 (2011)CrossRef
13.
Lopez, L.I., Bardallo, J.M., De Vega, M.A., Peregrin, A.: Regal-tc: a distributed genetic algorithm for concept learning based on regal and the treatment of counterexamples. Soft Comput. 15(7), 1389–1403 (2011)CrossRef
14.
Trelles, O., Prins, P., Snir, M., Jansen, R.C.: Big data, but are we ready? Nat. Rev. Genetics 12(3), 224–224 (2011)CrossRef
15.
Stoica, I.: A berkeley view of big data: algorithms, machines and people. In: UC Berkeley EECS Annual Research, Symposium (2011)
16.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz,  N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21–32 (2011)
17.
Borthakur, D.: The hadoop distributed file system: architecture and design. Hadoop Project Website 11, 21 (2007)
18.
Caragea, D., Silvescu, A., Honavar, V.: Analysis and synthesis of agents that learn from distributed dynamic data sources. In: Wermter, S., Austin, J., Willshaw D.J. (eds.) Emergent Neural Computational Architectures Based on Neuroscience, pp. 547–559. Springer-Verlag, Berlin (2001)
19.
Tsoumakas, G., Vlahavas, I.: Distributed data mining. In: Erickson J. (ed.) Database Technologies: Concepts, Methodologies, Tools, and Applications, pp. 157–171. IGI Global, Hershey (2009)
20.
Kargupta, H., Byung-Hoon, D.H., Johnson, E.: Collective Data Mining: A New Perspective Toward Distributed Data Analysis. In: Kargupta, H., Chan, P. (eds.) Advances in Distributed and Parallel Knowledge Discovery, AAAI Press/The MIT Press, Menlo Park (1999)
21.
Dietterich, T.: Ensemble methods in machine learning. In: Gayar, N.E., Kittler, J., Roli, F. (eds.) Multiple classifier systems, pp. 1–15. Springer, New York (2000)
22.
Guo, Y., Sutiwaraphun, J.: Probing knowledge in distributed data mining. In: Zhong, N., Zhou, L. (eds.) Methodologies for Knowledge Discovery and Data Mining, pp. 443–452. Springer, Berlin (1999)
23.
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)CrossRef
24.
Chan, P.K., Stolfo, S.J.: Toward parallel and distributed learning by meta-learning. In: AAAI Workshop in Knowledge Discovery in Databases, pp. 227–240 (1993)
25.
26.
Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16(1), 66–75 (1994)CrossRef
27.
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRef
28.
29.
Breiman, L.: Pasting small votes for classification in large databases and on-line. Mach. Learn. 36(1), 85–103 (1999)CrossRef
30.
31.
Chawla, N., Hall, L., Bowyer, K., Moore, T., Kegelmeyer, W.: Distributed pasting of small votes. In: Gayar, N.E., Kittler, J., Roli, F. (eds.) Multiple Classifier Systems, pp. 52–61. Springer, New York (2002)
32.
Tsoumakas G., Vlahavas, I.: Effective stacking of distributed classifiers. In: ECAI, pp. 340–344 (2002)
33.
Lazarevic, A., Obradovic, Z.: Boosting algorithms for parallel and distributed learning. Distrib. Parallel Databases 11(2), 203–229 (2002)MATHCrossRef
34.
Freund,Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann Publishers, Inc., San Francisco (1996)
35.
Hand, D.J., Mannila, H., Smyth, P.: Principles of data mining. The MIT press, Cambridge (2001)
36.
Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective voting of heterogeneous classifiers. In: Machine Learning: ECML, pp. 465–476 (2004)
37.
Woods, K., Kegelmeyer, W.P. Jr., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 405–410 (1997)
38.
Gama, J., Rodrigues, P.P., Sebastião, R.: Evaluating algorithms that learn from data streams. In: Proceedings of the 2009 ACM symposium on Applied Computing (ACM), pp. 1496–1500 (2009)
39.
Urban, P., Défago, X., Schiper, A.: Neko: a single environment to simulate and prototype distributed algorithms. In: 15th International Conference on Information Networking, pp. 503–511. IEEE (2001)
40.
Tsoumakas, G., Angelis, L., Vlahavas, I.: Clustering classifiers for knowledge discovery from physically distributed databases. Data Knowl. Eng. 49(3), 223–242 (2004)CrossRef
41.
Sonnenburg, S., Franc, V., Yom-Tov, E., Sebag, M.: PASCAL large scale Learning challenge. In: 25th International Conference on Machine Learning (ICML2008) Workshop. http://​largescale.​first.​fraunhofer.​de. J. Mach. Learn. Res. 10, 1937–1953 (2008)
42.
Peteiro-Barral, D., Bolon-Canedo, V., Alonso-Betanzos, A., Guijarro-Berdinas, B., Sanchez-Marono, N.: Scalability analysis of filter-based methods for feature selection. In: Howlett R. (ed.) Advances in Smart Systems Research, vol. 2, no. 1, pp. 21–26. Future Technology Publications, Shoreham-by-sea, UK (2012)
Metadaten
Titel
A survey of methods for distributed machine learning
verfasst von
Diego Peteiro-Barral
Bertha Guijarro-Berdiñas
Publikationsdatum
01.03.2013
Verlag
Springer-Verlag
Erschienen in
Progress in Artificial Intelligence / Ausgabe 1/2013
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-012-0035-5

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