Abstract
The regression is one of the fundamental problems in data mining, which is central to many applications of information technology. Various approaches have been presented for regression problem nowadays. However, many problems still exist, such as efficiency and model selection problem. This paper proposes a new approach to regression problem, visual regression problem (VRA) in order to resolve these problems. The core idea is to transfer the regression problem to classification problem based on Ancona theorem, which gives the mathematical equivalence between two problems; and then use visual classification approach, which is an efficient classification approach developed based on mimicking human sensation and perception principle, to solve the transformed classification problem and get an implicit regression function; and finally utilize some mathematical skills to obtain the explicit solution of the regression problem. We also provide a series of simulations to demonstrate that the proposed approach is not only effective but also efficient.
This research was supported by the NSFC project under contract 10371097.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lehmann, T., Gonner, C., Spitzer, K.: Survey: interpolation methods in medical image processing. IEEE T. Med. Imaging 18, 1049–1075 (1999)
Angleton, G., Bonham, D.: Least squares regression vs. Geometric mean regressionfor ecotoxicology studies. Appl. Math. Comput. 72, 21–32 (1995)
Leski, M.: ε-insensitive fuzzy c-regression models: introduction to ε-insensitive fuzzy modeling. IEEE T. Syst. Man CY. B. 34, 4–16 (2004)
Lee, E., Chee, L., Yuen, R., Lo, S.: A hybrid neural network model for noisy data regression. IEEE T. Syst. Man CY. B. 34, 951–960 (2004)
Scholkopf, B., Burges, C., Smola, A.: Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)
Ancona, N.: Classification Properties of Support Vector Machines for Regression. Technical Report RI-IESI/CNR - Nr. 02/99 (1999)
Xu, Z.B., Meng, D.Y., Jing, W.F.: A New Approach for Classification: Visual Simulation Point of View. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 1–7. Springer, Heidelberg (2005)
Coren, S., Ward, L., Enns, J.: Sensation and Perception. Harcourt Brace College Publishers (1994)
Rasch, G.: Robust boosting via convex optimization. PhD thesis, University ofPotsdam, Neues Palais 10, 14469 Potsdam, Germany (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meng, D., Xu, C., Jing, W. (2005). A New Approach for Regression: Visual Regression Approach. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_20
Download citation
DOI: https://doi.org/10.1007/11596448_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
eBook Packages: Computer ScienceComputer Science (R0)