SVM-based Sketch Recognition: Which Hyperparameter Interval to Try

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Kemal Tugrul Yesilbek, Cansu Sen, Serike Cakmak, and T. Metin Sezgin: SVM-based Sketch Recognition: Which Hyperparameter Interval to Try? In: Computational Aesthetics 2015 SBIM'15, 117-121.



Hyperparameters are among the most crucial factors that affect the performance of machine learning algorithms. In general, there is no direct method for determining a set of satisfactory parameters, so hyperparameter search needs to be conducted each time a model is to be trained. In this work, we analyze how similar hyperparameters perform across various datasets from the sketch recognition domain. Results show that hyperparameter search space can be reduced to a subspace despite differences in characteristics of datasets.

Extended Abstract


author = {Yesilbek, K. T. and Sen, C. and Cakmak, S. and Sezgin, T. M.},
title = {SVM-based Sketch Recognition: Which Hyperparameter Interval to Try?},
booktitle = {Proceedings of the Workshop on Sketch-Based Interfaces and Modeling},
series = {SBIM '15},
year = {2015},
location = {Istanbul, Turkey},
pages = {117--121},
numpages = {5},
url = { },
acmid = {2810218},
publisher = {Eurographics Association},
address = {Aire-la-Ville, Switzerland, Switzerland},

Used References

1 Chih-Chung Chang , Chih-Jen Lin, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST), v.2 n.3, p.1-27, April 2011 [doi>10.1145/1961189.1961199]

2 Olivier Chapelle , Vladimir Vapnik , Olivier Bousquet , Sayan Mukherjee, Choosing Multiple Parameters for Support Vector Machines, Machine Learning, v.46 n.1-3, p.131-159, 2002 [doi>10.1023/A:1012450327387]

3 Mathias Eitz , James Hays , Marc Alexa, How do humans sketch objects?, ACM Transactions on Graphics (TOG), v.31 n.4, p.1-10, July 2012 [doi>10.1145/2185520.2185540]

4 Tobias Glasmachers , Christian Igel, Gradient-Based Adaptation of General Gaussian Kernels, Neural Computation, v.17 n.10, p.2099-2105, October 2005 [doi>10.1162/0899766054615635]

5 S. S. Keerthi, Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms, IEEE Transactions on Neural Networks, v.13 n.5, p.1225-1229, September 2002 [doi>10.1109/TNN.2002.1031955]

6 {LXKJ13} Lin L., Xiaolong Z., Kai Z., Jun L.: Bilinear grid search strategy based support vector machines learning method. 1

7 {Man97} Manual F.: 101-5-1, operational terms and graphics. Washington, DC, Department of the Army 30, 3 (1997). 2

8 {Nar07} Narzisi G.: An experimental multi-objective study of the svm model selection problem. Courant Inst. Math. Sci (2007). 1

9 {NWV08} Niels R., Willems D., Vuurpijl L.: The nicicon database of handwritten icons for crisis management. Nijmegen Institute for Cognition and Information Radboud University Nijmegen, Nijmegen, The Netherlands (2008). 2

10 {OD09} Ouyang T. Y., Davis R.: A visual approach to sketched symbol recognition. 2

11 {Oze13} Ozen B.: A Simple Resource-Aware Approach to Sketch Recognizers via Style Identification. Master's thesis, Koc University, Istanbul, Turkey, 2013. 2

12 {Sta03} Staelin C.: Parameter selection for support vector machines. Hewlett-Packard Company, Tech. Rep. HPL-2002-354R1 (2003). 1

13 R. Sinan Tumen , M. Emre Acer , T. Metin Sezgin, Feature extraction and classifier combination for image-based sketch recognition, Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium, June 07-10, 2010, Annecy, France

14 AydıN Ulaş , Olcay Taner YıLdıZ , Ethem AlpaydıN, Cost-conscious comparison of supervised learning algorithms over multiple data sets, Pattern Recognition, v.45 n.4, p.1772-1781, April, 2012 [doi>10.1016/j.patcog.2011.10.005]

15 Yiyan Xiong , Joseph J. LaViola, Jr., Revisiting ShortStraw: improving corner finding in sketch-based interfaces, Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling, August 01-02, 2009, New Orleans, Louisiana [doi>10.1145/1572741.1572759]


Full Text

intern file

Sonstige Links