DocumentCode :
2478717
Title :
RANSAC-SVM for large-scale datasets
Author :
Nishida, Kenji ; Kurita, Takio
Author_Institution :
Neurosci. Res. Inst., Nat. Inst. of Adv. Ind. Sci. & Technol., Tsukuba
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
1
Lastpage :
4
Abstract :
Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem, we propose RANSAC-SVM method, which trains a number of small SVMs for randomly selected subsets of training set, while tuning their parameters to fit SVMs to whole training set. RANSAC-SVM achieves good generalization performance, which close to the Bayesian estimation, with small subset of the training samples, and outperforms the full SVM solution in some condition.
Keywords :
learning (artificial intelligence); random processes; support vector machines; very large databases; Bayesian estimation; RANSAC-SVM method; large-scale dataset; random sample consensus; support vector machine; training set; Bayesian methods; Computational complexity; Computer industry; Hydrogen; Kernel; Large-scale systems; Neuroscience; Remote sensing; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
ISSN :
1051-4651
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2008.4761280
Filename :
4761280
Link To Document :
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