-
1
-
-
85056143670
-
-
Accessed 27 Sept 2010 School of Information and Management and Systems. How much information?
-
School of Information and Management and Systems. How much information? http://www2. sims. berkeley. edu/research/projects/how-much-info/internet. html (2000). Accessed 27 Sept 2010.
-
(2000)
-
-
-
2
-
-
85056102702
-
-
D-Lib Magazine. A research library based on the historical collections of the Internet Archive Accessed 27 Oct 2010
-
D-Lib Magazine. A research library based on the historical collections of the Internet Archive. http://www. dlib. org/dlib/february06/arms/02arms. html (2006). Accessed 27 Oct 2010.
-
(2006)
-
-
-
3
-
-
0003460351
-
-
PhD thesis, School of Computer Science, University of Technology, Sydney, Australia
-
Catlett, J.: Megainduction: machine learning on very large databases. PhD thesis, School of Computer Science, University of Technology, Sydney, Australia (1991).
-
(1991)
Megainduction: Machine learning on very large databases
-
-
Catlett, J.1
-
5
-
-
33847314004
-
Large scale learning with string kernels
-
In: Bottou, L., Chapelle, O., DeCoste, D., Weston, J. (eds.) MIT Press, Cambridge
-
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).
-
(2007)
Large Scale Kernel Machines
, pp. 73-104
-
-
Sonnenburg, S.1
Ratsch, G.2
Rieck, K.3
-
6
-
-
67149116890
-
Scaling up classifiers to cloud computers
-
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).
-
(2008)
Proceedings of the 8th IEEE International Conference on Data Mining (ICDM)
, pp. 472-481
-
-
Moretti, C.1
Steinhaeuser, K.2
Thain, D.3
Chawla, N.V.4
-
7
-
-
85162014179
-
A randomized algorithm for large scale support vector learning
-
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).
-
(2008)
Proceedings of Advances in Neural Information Processing Systems (NIPS)
, pp. 793-800
-
-
Krishnan, S.1
Bhattacharyya, C.2
Hariharan, R.3
-
8
-
-
71149105669
-
Large-scale deep unsupervised learning using graphics processors
-
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).
-
(2009)
Proceedings of the 26th Annual International Conference on Machine Learning (ICML)
, pp. 873-880
-
-
Raina, R.1
Madhavan, A.2
Ng, A.Y.3
-
9
-
-
0141771188
-
A survey of methods for scaling up inductive algorithms
-
Provost, F., Kolluri, V.: A survey of methods for scaling up inductive algorithms. Data Min. Knowl. Discov. 3(2), 131-169 (1999).
-
(1999)
Data Min. Knowl. Discov.
, vol.3
, Issue.2
, pp. 131-169
-
-
Provost, F.1
Kolluri, V.2
-
10
-
-
0000662737
-
Search-intensive concept induction
-
Giordana, A., Neri, F.: Search-intensive concept induction. Evol. Comput. 3(4), 375-416 (1995).
-
(1995)
Evol. Comput.
, vol.3
, Issue.4
, pp. 375-416
-
-
Giordana, A.1
Neri, F.2
-
11
-
-
0036808967
-
Now g-net: learning classification programs on networks of workstations
-
Anglano, C., Botta, M.: Now g-net: learning classification programs on networks of workstations. IEEE Trans. Evol. Comput. 6(5), 463-480 (2002).
-
(2002)
IEEE Trans. Evol. Comput.
, vol.6
, Issue.5
, pp. 463-480
-
-
Anglano, C.1
Botta, M.2
-
12
-
-
77957902499
-
Efficient distributed genetic algorithm for rule extraction
-
Rodríguez, M., Escalante, D. M., Peregrín, A.: Efficient distributed genetic algorithm for rule extraction. Appl. Soft Comput. 11(1), 733-743 (2011).
-
(2011)
Appl. Soft Comput.
, vol.11
, Issue.1
, pp. 733-743
-
-
Rodríguez, M.1
Escalante, D.M.2
Peregrín, A.3
-
13
-
-
79958076987
-
Regal-tc: a distributed genetic algorithm for concept learning based on regal and the treatment of counterexamples
-
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).
-
(2011)
Soft Comput.
, vol.15
, Issue.7
, pp. 1389-1403
-
-
Lopez, L.I.1
Bardallo, J.M.2
de Vega, M.A.3
Peregrin, A.4
-
14
-
-
79951811877
-
Big data, but are we ready?
-
Trelles, O., Prins, P., Snir, M., Jansen, R. C.: Big data, but are we ready? Nat. Rev. Genetics 12(3), 224-224 (2011).
-
(2011)
Nat. Rev. Genetics
, vol.12
, Issue.3
, pp. 224
-
-
Trelles, O.1
Prins, P.2
Snir, M.3
Jansen, R.C.4
-
15
-
-
84876578081
-
A berkeley view of big data: algorithms, machines and people
-
Symposium
-
Stoica, I.: A berkeley view of big data: algorithms, machines and people. In: UC Berkeley EECS Annual Research, Symposium (2011).
-
(2011)
UC Berkeley EECS Annual Research
-
-
Stoica, I.1
-
16
-
-
79952320192
-
Big data, analytics and the path from insights to value
-
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).
-
(2011)
MIT Sloan Manag. Rev.
, vol.52
, Issue.2
, pp. 21-32
-
-
LaValle, S.1
Lesser, E.2
Shockley, R.3
Hopkins, M.S.4
Kruschwitz, N.5
-
17
-
-
70450136675
-
The hadoop distributed file system: architecture and design
-
Borthakur, D.: The hadoop distributed file system: architecture and design. Hadoop Project Website 11, 21 (2007).
-
(2007)
Hadoop Project Website
, vol.11
, pp. 21
-
-
Borthakur, D.1
-
18
-
-
0012255043
-
Analysis and synthesis of agents that learn from distributed dynamic data sources
-
In: Wermter, S., Austin, J., Willshaw D. J. (eds.) Springer-Verlag, Berlin
-
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).
-
(2001)
Emergent Neural Computational Architectures Based on Neuroscience
, pp. 547-559
-
-
Caragea, D.1
Silvescu, A.2
Honavar, V.3
-
19
-
-
79960148846
-
Distributed data mining
-
In: Erickson J. (ed.) IGI Global, Hershey
-
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).
-
(2009)
Database Technologies: Concepts, Methodologies, Tools, and Applications
, pp. 157-171
-
-
Tsoumakas, G.1
Vlahavas, I.2
-
20
-
-
0001282382
-
Collective Data Mining: A New Perspective Toward Distributed Data Analysis
-
Kargupta, H., Chan, P. (eds.)Menlo Park
-
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).
-
(1999)
Advances in Distributed and Parallel Knowledge Discovery, AAAI Press/The MIT Press
-
-
Kargupta, H.1
Byung-Hoon, D.H.2
Johnson, E.3
-
21
-
-
80053403826
-
Ensemble methods in machine learning
-
In: Gayar, N. E., Kittler, J., Roli, F. (eds.) Springer, New York
-
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).
-
(2000)
Multiple classifier systems
, pp. 1-15
-
-
Dietterich, T.1
-
22
-
-
79960133847
-
Probing knowledge in distributed data mining
-
In: Zhong, N., Zhou, L. (eds.) Springer, Berlin
-
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).
-
(1999)
Methodologies for Knowledge Discovery and Data Mining
, pp. 443-452
-
-
Guo, Y.1
Sutiwaraphun, J.2
-
25
-
-
22444454265
-
Combining classifiers: a theoretical framework
-
Kittler, J.: Combining classifiers: a theoretical framework. Pattern Anal. Appl. 1(1), 18-27 (1998).
-
(1998)
Pattern Anal. Appl.
, vol.1
, Issue.1
, pp. 18-27
-
-
Kittler, J.1
-
26
-
-
0028259890
-
Decision combination in multiple classifier systems
-
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).
-
(1994)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.16
, Issue.1
, pp. 66-75
-
-
Ho, T.K.1
Hull, J.J.2
Srihari, S.N.3
-
27
-
-
0032021555
-
On combining classifiers
-
Kittler, J., Hatef, M., Duin, R. P. W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226-239 (1998).
-
(1998)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.20
, Issue.3
, pp. 226-239
-
-
Kittler, J.1
Hatef, M.2
Duin, R.P.W.3
Matas, J.4
-
28
-
-
0026692226
-
Stacked generalization
-
Wolpert, D. H.: Stacked generalization. Neural Netw. 5(2), 241-259 (1992).
-
(1992)
Neural Netw.
, vol.5
, Issue.2
, pp. 241-259
-
-
Wolpert, D.H.1
-
29
-
-
0032634129
-
Pasting small votes for classification in large databases and on-line
-
Breiman, L.: Pasting small votes for classification in large databases and on-line. Mach. Learn. 36(1), 85-103 (1999).
-
(1999)
Mach. Learn.
, vol.36
, Issue.1
, pp. 85-103
-
-
Breiman, L.1
-
30
-
-
0004140497
-
-
Technical report. Available at
-
Breiman. L.: Out-of-bag estimation. Technical report. Available at ftp://ftp. stat. berkeley. edu/pub/users/breiman/OOBestimation. ps (1996).
-
(1996)
Out-of-bag estimation
-
-
Breiman, L.1
-
31
-
-
84947592660
-
Distributed pasting of small votes
-
In: Gayar, N. E., Kittler, J., Roli, F. (eds.) Springer, New York
-
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).
-
(2002)
Multiple Classifier Systems
, pp. 52-61
-
-
Chawla, N.1
Hall, L.2
Bowyer, K.3
Moore, T.4
Kegelmeyer, W.5
-
32
-
-
2342665431
-
Effective stacking of distributed classifiers
-
Tsoumakas G., Vlahavas, I.: Effective stacking of distributed classifiers. In: ECAI, pp. 340-344 (2002).
-
(2002)
ECAI
, pp. 340-344
-
-
Tsoumakas, G.1
Vlahavas, I.2
-
33
-
-
0036495711
-
Boosting algorithms for parallel and distributed learning
-
Lazarevic, A., Obradovic, Z.: Boosting algorithms for parallel and distributed learning. Distrib. Parallel Databases 11(2), 203-229 (2002).
-
(2002)
Distrib. Parallel Databases
, vol.11
, Issue.2
, pp. 203-229
-
-
Lazarevic, A.1
Obradovic, Z.2
-
34
-
-
0002978642
-
Experiments with a new boosting algorithm
-
Morgan Kaufmann Publishers, Inc., San Francisco
-
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).
-
(1996)
International Conference on Machine Learning
, pp. 148-156
-
-
Freund, Y.1
Schapire, R.E.2
-
36
-
-
22944467518
-
Effective voting of heterogeneous classifiers
-
Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective voting of heterogeneous classifiers. In: Machine Learning: ECML, pp. 465-476 (2004).
-
(2004)
Machine Learning: ECML
, pp. 465-476
-
-
Tsoumakas, G.1
Katakis, I.2
Vlahavas, I.3
-
37
-
-
0031121318
-
Combination of multiple classifiers using local accuracy estimates
-
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).
-
(1997)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.19
, Issue.4
, pp. 405-410
-
-
Woods, K.1
Kegelmeyer Jr., W.P.2
Bowyer, K.3
-
38
-
-
72949119714
-
Evaluating algorithms that learn from data streams
-
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).
-
(2009)
Proceedings of the 2009 ACM symposium on Applied Computing (ACM)
, pp. 1496-1500
-
-
Gama, J.1
Rodrigues, P.P.2
Sebastião, R.3
-
39
-
-
84949809105
-
Neko: a single environment to simulate and prototype distributed algorithms
-
IEEE
-
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).
-
(2001)
15th International Conference on Information Networking
, pp. 503-511
-
-
Urban, P.1
Défago, X.2
Schiper, A.3
-
40
-
-
2342557114
-
Clustering classifiers for knowledge discovery from physically distributed databases
-
Tsoumakas, G., Angelis, L., Vlahavas, I.: Clustering classifiers for knowledge discovery from physically distributed databases. Data Knowl. Eng. 49(3), 223-242 (2004).
-
(2004)
Data Knowl. Eng.
, vol.49
, Issue.3
, pp. 223-242
-
-
Tsoumakas, G.1
Angelis, L.2
Vlahavas, I.3
-
41
-
-
70049101797
-
PASCAL large scale Learning challenge. In: 25th International Conference on Machine Learning (ICML2008) Workshop
-
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).
-
(2008)
J. Mach. Learn. Res.
, vol.10
, pp. 1937-1953
-
-
Sonnenburg, S.1
Franc, V.2
Yom-Tov, E.3
Sebag, M.4
-
42
-
-
84891103387
-
Scalability analysis of filter-based methods for feature selection
-
In: Howlett R. (ed.) Future Technology Publications, Shoreham-by-sea, UK
-
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).
-
(2012)
Advances in Smart Systems Research
, vol.2
, Issue.1
, pp. 21-26
-
-
Peteiro-Barral, D.1
Bolon-Canedo, V.2
Alonso-Betanzos, A.3
Guijarro-Berdinas, B.4
Sanchez-Marono, N.5
|