A parallel incremental extreme SVM classifier

Neurocomputing

Volumn 74, Issue 16, 2011, Pages 2532-2540

  He, Qing ^a   Du, Changying ^a,b   Wang, Qun ^a,b   Zhuang, Fuzhen ^a,b   Shi, Zhongzhi ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Incremental extreme SVM (IESVM); MapReduce; Parallel extreme SVM (PESVM); Parallel incremental extreme SVM (PIESVM)

Indexed keywords

CLASSIFICATION ALGORITHM; DATA SETS; EVALUATION METRICS; GENERALIZATION PERFORMANCE; INCREMENTAL EXTREME SVM (IESVM); INCREMENTAL LEARNING; LARGE-SCALE PROBLEM; MAP-REDUCE; NEW MODEL; ONLINE LEARNING; ONLINE PROBLEMS; PARALLEL EXTREME SVM (PESVM); PARALLEL INCREMENTAL EXTREME SVM (PIESVM); PARALLEL VERSION; PROGRAMMING FRAMEWORK; SCALE-UP; SVM CLASSIFIERS; TRAINING DATA;

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS;

PARALLEL PROGRAMMING;

ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; EXTREME SUPPORT VECTOR MACHINE; INCREMENTAL EXTREME SUPPORT VECTOR MACHINE; INFORMATION PROCESSING; INTERMETHOD COMPARISON; ONLINE SYSTEM; PARALLEL EXTREME SUPPORT VECTOR MACHINE; PARALLEL INCREMENTAL EXTREME SUPPORT VECTOR MACHINE; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

EID: 80051586560     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.11.036     Document Type: Article

References (35)

1. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of International Joint Conference on Neural Networks (IJCNN2004), vol. 2, Budapest, Hungary, 25-29 July 2004, pp. 985-990.
2. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70(1-3):489-501.
3. Liu Q., He Q., Shi Z. Extreme support vector machine classifier. Lecture Notes in Computer Science 2008, vol. 5012:222-233.
4. B. Frénay, M. Verleysen, Using SVMs with randomised feature spaces: an extreme learning approach, in: Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 28-30 April 2010, pp. 315-320.
5. Huang G.-B., Ding X.-J., Zhou H.-M. Optimization method based extreme learning machine for classification. Neurocomputing 2010, 74(12):155-163.
6. Evgeniou T., Pontil M., Poggio T. Regularization networks and support vector machines. Advances in Large Margin Classifiers, 2000, 171-203.
7. Fung G., Mangasarian O.L. Proximal support vector machine classifiers. Proceedings of ACM SIGKDD 2001 2001, 77-86. ACM Press.
8. Mangasarian O.L., Wild E.W. Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Transaction on Pattern Analysis and Machine Intelligence 2006, 28(1):69-74.
9. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999, 9(3):293-300.
10. M.V. Heeswijk, Y. Miche, E. Oja, A. Lendasse, Solving large regression problems using an ensemble of GPU-accelerated ELMs, in: Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 28-30 April 2010, pp. 309-314.
11. Chang E., Zhu K., Wang H., Bai H., Li J., Qiu Z., Cui H. Parallelizing support vector machines on distributed computers. NIPS 20 2007, 257-264. MIT Press. J. Platt, D. Koller, Y. Singer, S. Roweis (Eds.).
12. Chan P.K., Stolfo S.J. Toward parallel and distributed learning by meta-learning. AAAI Workshop in Knowledge Discovery in Databases, 1993, 227-240.
13. Kruengkrai C., Jaruskulchai C. A parallel learning algorithm for text classification. Proceedings of ACM SIGKDD 2002 August 2002, 201-206. ACM Press.
14. Kivinen J., Smola A.J., Williamson R.C. Online learning with kernels. IEEE Transactions on Signal Processing 2002, 52(8):2165-2176.
15. Lan Y., Soh Y.-C., Huang G.-B. Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 2010, 73(16-18):3191-3199.
16. Huang G.-B., Chen L., Siew C.-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006, 17(4):879-892.
17. Liang N.-Y., Huang G.-B., Saratchandran P., Sundararajan N. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 2006, 17(6):1411-1423.
18. Feng G., Huang G.-B., Lin Q., Gay R. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks 2009, 20(8):1352-1357.
19. Huang G.-B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70(16-18):3056-3062.
20. Huang G.-B., Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing 2008, 71(16-18):3460-3468.
21. Ralaivola L., d'Alché-Buc F. Incremental support vector machine learning: a local approach. Lecture Notes in Computer Science 2001, vol. 2130:322-329.
22. Rüping S. Incremental learning with support vector machines. Proceedings of the 2001 IEEE International Conference on Data Mining 2001, 641-642. N. Cercone, T. Lin, X. Wu (Eds.).
23. N.A. Syed, H. Liu, K.K. Sung, Incremental learning with support vector machines, in: Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, 1999.
24. Dean J., Ghemawat S. MapReduce: simplified data processing on large cluster. Communications of the ACM 2008, 51(1):107-113.
25. Lammel R. Google's MapReduce programming model-revisited. Science of Computer Programming 2008, 70:1-30.
26. Chu C.-T., Kim S.K., Lin Y.-A., Yu Y., Bradski G.R., Ng A.Y., Olukotun K. Map-reduce for machine learning on multicore. NIPS 19 2006, 281-288. MIT Press. B. Scholkopf, J.C. Platt, T. Hoffman (Eds.).
27. Hadoop: Open source implementation of MapReduce http://hadoop.apache.org.
28. D. Borthakur, The Hadoop distributed file system: architecture and design, 2007.
29. G. Fung, O.L. Mangasarian, Incremental support vector machine classification, in: Proceedings of the Second SIAM International Conference on Data Mining, Arlington, Virginia, USA, 2002, pp. 247-260.
30. P.M. Murphy, D.W. Aha, UCI repository of machine learning databases, 1992.
31. Xu X., Jager J., Kriegel H.P. A fast parallel clustering algorithm for large spatial databases. Data Mining and Knowledge Discovery 1999, 3:263-290.
32. C. Ranger, R. Raghuraman, A. Penmetsa, G. Bradski, C. Kozyrakis, Evaluating MapReduce for multi-core and multiprocessor systems, in: Proceedings of the 13th International Symposium on High-Performance Computer Architecture (HPCA), Phoenix, AZ, 2007.
33. Vapnik V.N. Estimation of Dependencies Based on Empirical Data 1982, Springer, Berlin.
34. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer, New York.
35. Vapnik V.N. Statistical Learning Theory 1998, Wiley, New York.