Kernel selection in multi-class support vector machines and its consequence to the number of ties in majority voting method

Artificial Intelligence Review

Volumn 40, Issue 3, 2013, Pages 213-230

  Joutsijoki, Henry ^a   Juhola, Martti ^a  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Benthic macroinvertebrates; Classification; Kernel function; Majority voting method; Support vector machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); VECTORS;

BENTHIC MACROINVERTEBRATES; CLASSIFICATION METHODS; CLASSIFICATION RESULTS; CLASSIFICATION TESTS; KERNEL FUNCTION; KERNEL SELECTION; MULTI-CLASS SUPPORT VECTOR MACHINES; VOTING METHOD;

SUPPORT VECTOR MACHINES;

EID: 84885332005     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-011-9281-3     Document Type: Article

References (24)

1. Ärje J, Kärkkäinen S, Meissner K, Turpeinen T (2010) Statistical classification and proportion estimation - an application to a macroinvertebrate image database. In: Proceedings 20th international IEEE workshop on machine learning for signal processing (MLSP 2010), Kittilä, Finland, pp 373-378
2. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2: 121-167 (Pubitemid 128126769)
3. Christiani N, Shawe-Taylor J (2003) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
4. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20: 273-297
5. Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans on Electr Comp EC-14
6. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9: 1871-1874
7. Fletcher T (2009) Support vector machines explained. Available: http://www.cs.ucl.ac.uk/staff/T.Fletcher/. Accessed 17 June 2011
8. Hong JH, Cho SB (2008) A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification. Neurocomputing 71: 3275-3281
9. Hong JH, Min JK, Cho UK, Cho SB (2008) Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve bayes classifiers. Pattern Recognit 41: 662-671 (Pubitemid 47503256)
10. Howley T, Madden MG (2004) The genetic evolution of kernels for support vector machine classifiers. In: Proceedings of AICS-2004 15th Irish Conference on Artificial Intelligence & Cognitive Science
11. ImageJ: public domain Java-based image processing program. Available: http://rsbweb.nih.gov/ij/docs/index.html
12. Kiranyaz S, Gabbouj M, Pulkkinen J, Ince T, Meissner K (2010) Classification and retrieval on macroinvertebrate image databases using evolutionary RBF neural networks. In: Proceedings of the International Workshop on Advanced Image Technology (IWAIT), 11-12 January 2010 Kuala Lumpur, Malaysia
13. Kiranyaz S, Ince T, Pulkkinen J, Gabbouj M, Ärje J, Kärkkäinen S, Tirronen V, Juhola M, Turpeinen T, Meissner K (2011) Classification and retrieval on macroinvertebrate image databases. J Comput Biol Med 41(7): 463-472
14. Larios N, Deng H, Zhang W, Sarpola M, Yuen J, Paasch R, Moldenke A, Lytle DA, Correa SR, Mortensen EN, Shapiro LG, Diettrich TG (2008) Automated insect identification through concatenated histograms of local appearance features: features vector generation and region detection for deformable objects. Mach Vision Appl 19(2): 105-123 (Pubitemid 351177989)
15. Lytle DA, Martínez-Muñoz G, Zhang W, Larios N, Shapiro L, Paasch R, Moldenke A, Mortensen EN, Todorovic S, Diettrich TG (2010) Automated processing and identification of benthic invertebrate samples. J N Am Benthol Soc 29(3): 867-874
16. Moguerza JM, Muñoz A (2006) Support vector machines with applications. Stat Sci 21(3): 332-336
17. Platt JC (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. In: Technical Report MSR-TR-98-14 Microsoft Research
18. Riverlife project. Available in Finnish: http://www.ymparisto.fi/ riverlife, Accessed 20 June 2011
19. Sarpola MJ, Paasch RK, Diettrich TG, Lytle DA, Mortensen EN, Moldenke AR, Shapiro L (2008) An aquatic insect imaging device to automate insect classification. Trans ASABE 51(6): 2217-2225
20. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge
21. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9: 293-300
22. Theodoridis S, Koutroumbas K (2006) Pattern recognition, 3rd edn. Academic Press, Edinburgh
23. Tirronen V, Caponio A, Haanpää T, Meissner K (2009) Multiple order gradient feature for macroinvertebrate identification using support vector machines. In: Proceedings of the adaptive and natural computing algorithms. Lecture notes in computer Science, vol 5495, pp 489-497
24. Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, Berlin