Online multiple instance regression

doi:10.1088/1674-1056/22/9/098702

Abstract The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice, examples usually arrive in sequence. Therefore, the training step cannot be accomplished once. In this paper, an online multiple instance regression method “OnlineMIR” is proposed. OnlineMIR can not only predict the label of a new bag, but also update the current regression model with the latest arrived bag. The experimental results show that OnlineMIR achieves good performances on both synthetic and real data sets.

Keywords: mutiple instance regression online learning

Received: 16 January 2013
Revised: 08 March 2013
Accepted manuscript online:

PACS:	87.19.lo	(Information theory)
	87.15.Qt	(Sequence analysis)

Fund: Project supported by the China State Key Science and Technology Project on Marine Carbonate Reservoir Characterization (Grant No. 2011ZX05004-003), the National Basic Research Program of China (Grant No. 2013CB329503), the Beijing Municipal Education Commission Science, Technology Development Plan key project, China (Grant No. KZ201210005007), and China Postdoctoral Science Foundation (Grant No. 2012M521336).

Corresponding Authors: Zhang Chang-Shui
E-mail: zcs@mail.tsinghua.edu.cn

Cite this article:

Wang Zhi-Gang (王志岗), Zhao Zeng-Shun (赵增顺), Zhang Chang-Shui (张长水) Online multiple instance regression 2013 Chin. Phys. B 22 098702

[1]	Dietterich T G, Lathrop R H and Lozano-Pérez T 1997 Artif. Intell. 89 31
[2]	Ray S and Page D 2001 Workshop of International Conference on Machine Learning, June 28-July 1, 2001, Williamstown, USA, pp. 425-432
[3]	Andrews S, Tsochantaridis I and Hofmann T 2003 Advances in Neural Information Processing Systems, December 9, Vancouver, Canada, pp. 577-584
[4]	Chen Y X and Wang J Z 2004 J. Mach. Learn. Res. 5 939
[5]	Chen Y X, Bi J and Wang J Z 2006 IEEE Trans. Pattern Anal. Mach. Intell. 28 1931
[6]	Fung G, Dundar M, Krishnapuram B and Rao R B 2007 Advances in Neural Information Processing Systems, December 4, Vancouver, Canada, pp. 425-433
[7]	Zhou Z H, Sun Y Y and Li Y F 2009 Proceedings of the 26th Annual International Conference on Machine Learning, June 14-18, Montreal, Canada, pp. 1249-1256
[8]	Babenko B, Yang M H and Belongie S 2011 IEEE Trans. Pattern Anal. Mach. Intell. 33 1619
[9]	Li M, Kwok J T and Lu B L 2010 Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, June 13-18, San Francisco, USA, pp. 1395-1401
[10]	Zeisl B, Leistner C, Saffari A and Bischof H Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, USA, pp. 1879-1886
[11]	Zhou Z H 2010 Signal Process 2 2
[12]	Amar R A, Dooly D R, Goldman S A and Zhang Q 2001 Workshop of International Conference on Machine Learning, June 28-July 1, Williamstown, USA, pp. 3-10
[13]	Dooly D R, Zhang Q, Goldman S A and Amar R A 2003 J. Mach. Learn. Res. 3 651
[14]	Wagstaff K L and Lane T 2007 Workshop of International Conference on Machine Learning, June 20-24, 2007, Corvallis, USA, pp. 444-451
[15]	Wagstaff K L, Lane T and Roper A 2008 Workshop of IEEE International Conference on Data Mining, December 15-19, 2008, Pisa, Italy, pp. 291-300
[16]	Sun Z H and Jiang F 2010 Chin. Phys. B 19 110502
[17]	Parrella F 2007 A Thesis Presented for the Degree of Information Science
[18]	Cauwenberghs G and Poggio T 2001 Advances in Neural Information Processing Systems, December 3, 2001, Vancouver, Canada, pp. 409-415
[19]	Galford G L, Mustard J F, Melillo J, Gendrin A, Cerri C C and Cerri C E P 2008 Remote Sens. Environ. 112 576
[20]	Meng Q F, Chen Y H and Peng Y H 2009 Chin. Phys. B 18 2194

[1]	Ridge regression energy levels calculation of neutral ytterbium (Z = 70) Yushu Yu(余雨姝), Chen Yang(杨晨), and Gang Jiang(蒋刚). Chin. Phys. B, 2023, 32(3): 033101.
[2]	Quantum partial least squares regression algorithm for multiple correlation problem Yan-Yan Hou(侯艳艳), Jian Li(李剑), Xiu-Bo Chen(陈秀波), and Yuan Tian(田源). Chin. Phys. B, 2022, 31(3): 030304.
[3]	The interaction between stratospheric monthly mean regional winds and sporadic-E Kenan Çetin, Osman Özcan, Serhat Korlaelçi. Chin. Phys. B, 2017, 26(3): 039401.
[4]	Quantitative analysis of ammonium salts in coking industrial liquid waste treatment process based on Raman spectroscopy Ya-Nan Cao(曹亚南), Gui-Shi Wang(王贵师), Tu Tan(谈图), Ting-Dong Cai(蔡廷栋), Kun Liu(刘锟), Lei Wang(汪磊), Gong-Dong Zhu(朱公栋), Jiao-Xu Mei(梅教旭). Chin. Phys. B, 2016, 25(10): 107403.
[5]	Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method Sun Zhong-Hua(孙重华) and Jiang Fan(江凡). Chin. Phys. B, 2010, 19(11): 110502.
[6]	Small-time scale network traffic prediction based on a local support vector machine regression model Meng Qing-Fang(孟庆芳), Chen Yue-Hui(陈月辉), and Peng Yu-Hua(彭玉华). Chin. Phys. B, 2009, 18(6): 2194-2199.
[7]	Prediction of chaotic time series based on modified minimax probability machine regression Sun Jian-Cheng(孙建成). Chin. Phys. B, 2007, 16(11): 3262-3270.
[8]	A novel technique for predicting ionizing radiation effects of commercial MOS devices Zhang Guo-Qiang (张国强), Guo Qi (郭旗), Erkin (艾尔肯), Lu Wu (陆妩), Ren Di-Yuan (任迪远). Chin. Phys. B, 2004, 13(6): 948-953.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Online multiple instance regression

Cite this article:

Online attention