SVM-based Sketch Recognition: Which Hyperparameter Interval to Try
Inhaltsverzeichnis
Reference
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.
DOI
http://dx.doi.org/10.2312/exp.20151184
Abstract
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
Bibtex
@inproceedings{Yesilbek:2015:SSR:2810210.2810218,
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 = {http://dl.acm.org/citation.cfm?id=2810210.2810218 http://de.evo-art.org/index.php?title=SVM-based_Sketch_Recognition:_Which_Hyperparameter_Interval_to_Try },
acmid = {2810218},
publisher = {Eurographics Association},
address = {Aire-la-Ville, Switzerland, Switzerland},
}
Used References
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Links
Full Text
http://iui.ku.edu.tr/sezgin_publications/2015/SVM_Hyperparameter_Expressive15.pdf
Sonstige Links
http://dl.acm.org/citation.cfm?id=2810210.2810218&coll=DL&dl=GUIDE&CFID=724111209&CFTOKEN=48939661
