全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于SVC和SVR约束组合的迁移学习分类算法

DOI: 10.13195/j.kzyjc.2013.0520, PP. 1021-1026

Keywords: 支持向量机,支持向量分类,支持向量回归,迁移学习

Full-Text   Cite this paper   Add to My Lib

Abstract:

根据迁移学习思想,针对分类问题,以支持向量机(SVM)模型为基础提出一种新的迁移学习分类算法CC-TSVM.该方法以邻域间的分类超平面为纽带实现源域对目标域的迁移学习.具体地,以支持向量分类的约束条件完成对目标域数据的学习,获取分类超平面参数,再以支持向量回归的约束条件有效利用源域数据矫正目标域超平面参数,并在上述组合约束的共同作用下实现邻域间迁移,提高分类器性能.在人工和真实数据集上的实验表明,所提出算法具有良好的迁移能力和优越的分类性能.

References

[1]  Liao X, Xue Y, Carin L. Logistic regression with an auxiliary data source[C]. Proc of the 22nd Int Conf on Machine Learning Bonn. Germany, 2005: 505-512.
[2]  Freund Y, Schapire R E. A decision theoretic generalization of on-line learning and an application to boosting[J]. J of Computer and System Sciences, 1997, 55(1): 119-139.
[3]  Dai W, Yang Q, Xue G, et al. Boosting for transfer learning[C]. Proc of the 24th Int Conf on Machine Learning. New York: ACM, 2007: 193-200.
[4]  Quanz B, Huan J. Large margin transductive transfer learning[C]. Proc of the 18th ACM Conf Information and knowledge management. New York: ACM, 2009: 1327-1336.
[5]  Ablavsky V H, Becker C J, Fua P. Transfer learning by sharing support vectors[R]. No.EPFL REPORT 181360, 2012.
[6]  Ling X, Dai W Y, Xue G R, et al. Spectral domain-transfer learning[C]. Proc of the 14th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. Las Vegas, 2008: 488-496.
[7]  Mihakova L, Mooney R J. Transfer learning by mapping with minimal target data[C]. Proc of the AAAI-2008 Workshop on Transfer Learning for Complex Tasks. Chicago, 2008.
[8]  Davis J, Domingos P. Deep transfer via second-order Markov logic[C]. Proc of the 26th Annual Inte Conf on Machine Learning. ACM, 2009: 217-224.
[9]  Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation and signal processing[C]. Neural Information Processing Systems. Cambridge: MIT Press, 1997: 281-287.
[10]  Wang Z, Song Y, Zhang C. Transferred dimensionality rednction[M]. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer, 2008: 550-565.
[11]  Body S, Vandenberghe L. Convex optimization[M]. Cambridge: Cambridge University Press, 2004.
[12]  Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Trans on Knowledge and Data Engineering, 2010 , 22(10): 1345-1359.
[13]  Caruana R. Multitask learning[J]. Machine Learning, 1997, 28(1): 41-75.
[14]  Raina R, Battle A, Lee H, et al. Self-taught learning: Transfer learning from unlabeled data[C]. Proc of the 24th Int Conf on Machine Learning. Corvalis, 2007: 759-766.
[15]  Daum’Eiii H, Marcu D. Domain adaptation for statistical classifiers[J]. J of Artifical Intelligence Research, 2006, 26: 101-126.
[16]  Zadrozny B. Learning and evaluating classifiers under sample selection bias[C]. Proc of the 21st Int Conf on Machine Learning. Banff, 2004: 114-122.
[17]  Ben-David S, Schuller R. Exploiting task relatedness for multiple task learning[C]. Proc of the 16th Annual Conf on Learning Theory. San Francisco: Morgan Kaufmann, 2003: 825-830.
[18]  Wu P, Dietterich T G. Improving svm accuracy by training on auxiliary data sources[C]. Proc of the 21st Int Conf on Machine Learning. Banff, 2004: 69.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133