Support vector machines with manifold learning and probabilistic space projection for tourist expenditure analysis

International Journal of Computational Intelligence Systems

Volumn 2, Issue 1, 2009, Pages 17-26

  Xu, Xin ^a   Law, Rob ^b   Wu, Tao ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Data mining; Feature projection; ISOMAP; Manifold learning; Scale transformation; SVMs; Tourism data analysis

Indexed keywords

DATA HANDLING; DATA MINING; INFORMATION ANALYSIS; LEARNING SYSTEMS; MAPPING; METADATA; SUPPORT VECTOR MACHINES; VECTOR SPACES;

FEATURE PROJECTION; ISOMAP; MANIFOLD LEARNING; SCALE TRANSFORMATION; SVMS;

CLASSIFICATION (OF INFORMATION);

EID: 77951493827     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.1080/18756891.2009.9727636     Document Type: Article

References (17)

1. Braun, B. M., and Rungeling, B., 1992. The relative economic impact of convention and tourist on a regional economy:a case study. International Journal Hospitality Management, 11 (1):65–71.
2. Lawson, F. R., 1982. Trends in business tourism management. Tourism Management, 3 (4):298–302.
3. Hong Kong Tourism Board. 2005a. Statistics on Conventions & Exhibitions 2004 Accessed on February 3, 2006http://partnernet.hktourismboard.com/
4. Hong Kong Tourism Board. 2005b. Visitor Profile Report 2004 Accessed on February 3, 2006http://partnernet.hktourismboard.com/
5. Law, R., Bauer, T., Weber, K., and Tse, T., 2006. Towards a rough classification of business travelers. Lecture Notes in Artificial Intelligence, 4093:135–142.
6. Xu, X., Law, R., and Wu, T., 2007. Classification of business travelers using SVMs combined with kernel principal component analysis. Advanced data mining and applications, 4632:524–532. LNCS
7. Balasubramanian, M., Schwartz, E. L., Tenenbaum, J. B., de Silva, V., and Langford, J. C., 2002. The Isomap algorithm and topological stability. Science, 295 (5552):7a
8. Hui, E. L. L., 2001. McKercher. Operational issues in marketing research:an example of the Omnibus tourism survey. Pacific Tourism Review, 5 (1/2):5–13.
9. Schölkopf, B., and Smola, A., 2002. Learning With Kernels, Cambridge, MA:MIT Press.
10. Vapnik, V., 1995. The Nature of Statistical Learning Theory, New York:Springer Verlag.
11. Pai, P.-F., and Hong, W.-C., 2005. An improved neural network model in forecasting arrivals. Annals of Tourism Research, 32 (4):1138–1141.
12. Law, R., 1999. A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20:80–97.
13. Chen, K.-Y., and Wang, C.-H., 2007. Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28 (1):215–226.
14. Roweis, Sam, and Saul, Lawrence. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290 (5500) Dec.22:2323–2326.
15. Platt, J., 1999. “ Fast Training of Support Vector Machines using Sequential Minimal Optimization ”. In Advances in Kernel Methods—Support Vector Learning, Edited by:Scholkopf, B., Burges, C. J. C., and Smola, A. J., 185–208. Cambridge:MIT Press.
16. Dietterich, T. G., and Bakiri, G., 1995. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263–286.
17. SVM and Kernel Methods Matlab Toolbox http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/ index.html