Ming Tan

	2008
20		Zhenqiu Liu, Ronald B. Gartenhaus, Ming Tan, Feng Jiang, Xiaoli Jiao: Gene and pathway identification with Lp penalized Bayesian logistic regression. BMC Bioinformatics 9: (2008)
19		Guo-Liang Tian, Kai Wang Ng, Ming Tan: EM-type algorithms for computing restricted MLEs in multivariate normal distributions and multivariate t-distributions. Computational Statistics & Data Analysis 52(10): 4768-4778 (2008)
18		Guo-Liang Tian, Man-Lai Tang, Hong-Bin Fang, Ming Tan: Efficient methods for estimating constrained parameters with applications to regularized (lasso) logistic regression. Computational Statistics & Data Analysis 52(7): 3528-3542 (2008)
	2007
17		Man-Lai Tang, Kai Wang Ng, Guo-Liang Tian, Ming Tan: On improved EM algorithm and confidence interval construction for incomplete r. Computational Statistics & Data Analysis 51(6): 2919-2933 (2007)
	2006
16		Bocheng Zhong, Jianghong Han, Zhaofang Du, Yuan Ming, Ming Tan: Game Based Flow Rate Control for Networks. ICICIC (1) 2006: 401-404
15		Ming Tan, Guo-Liang Tian, Kai Wang Ng: Hierarchical models for repeated binary data using the IBF sampler. Computational Statistics & Data Analysis 50(5): 1272-1286 (2006)
14		Zhenqiu Liu, Shili Lin, Ming Tan: Genome-Wide Tagging SNPs with Entropy-Based Monte Carlo Method. Journal of Computational Biology 13(9): 1606-1614 (2006)
	2001
13		Steve Gallant, Gregory Piatetsky-Shapiro, Ming Tan: Value-based data mining and web mining for CRM. KDD Tutorials 2001
	2000
12		Ming Tan, Johnson Lee, Hao Xu, Joshua Introne, Christopher J. Matheus: Wireless usage analysis for capacity planning and beyond: a data warehouse approach. NOMS 2000: 905-917
	1999
11		Ming Tan, Hao Xu, Johnson Lee: DART: A Decision Support System for Cellular Networks Usage Analysis. IC-AI 1999: 479-485
	1996
10		Ming Tan, Carol Lafond, Gabriel Jakobson, Gary Young: Supporting Performance and Configuration Management of GTE Cellular Networks. AAAI/IAAI, Vol. 2 1996: 1556-1563
	1993
9		Ming Tan: Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents. ICML 1993: 330-337
8		Ming Tan: Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics. Machine Learning 13: 7-33 (1993)
	1991
7		Ming Tan: Cost-Sensitive Reinforcement Learning for Adaptive Classification and Control. AAAI 1991: 774-780
6		Ming Tan: Learning a Cost-Sensitive Internal Representation for Reinforcement Learning. ML 1991: 358-362
5		Ming Tan, Jeffrey C. Schlimmer: A cost-sensitive machine learning method for the approach and recognize task. Robotics and Autonomous Systems 8(1-2): 31-45 (1991)
	1990
4		Ming Tan, Jeffrey C. Schlimmer: Two Case Studies in Cost-Sensitive Concept Acquisition. AAAI 1990: 854-860
	1989
3		Ming Tan, Jeffrey C. Schlimmer: Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition. ML 1989: 392-395
	1988
2		Ming Tan, Larry J. Eshelman: Using Weighted Networks to Represent Classification Knowledge in Noisy Domains. ML 1988: 121-134
	1987
1		Larry J. Eshelman, Damien Ehret, John P. McDermott, Ming Tan: MOLE: A Tenacious Knowledge-Acquisition Tool. International Journal of Man-Machine Studies 26(1): 41-54 (1987)

Coauthor Index

1	Zhaofang Du	[16]
2	Damien Ehret	[1]
3	Larry J. Eshelman	[1] [2]
4	Hong-Bin Fang	[18]
5	Steve Gallant	[13]
6	Ronald B. Gartenhaus	[20]
7	Jianghong Han	[16]
8	Joshua Introne	[12]
9	Gabriel Jakobson	[10]
10	Feng Jiang	[20]
11	Xiaoli Jiao	[20]
12	Carol Lafond	[10]
13	Johnson Lee	[11] [12]
14	Shili Lin	[14]
15	Zhenqiu Liu	[14] [20]
16	Christopher J. Matheus	[12]
17	John P. McDermott	[1]
18	Yuan Ming	[16]
19	Kai Wang Ng	[15] [17] [19]
20	Gregory Piatetsky-Shapiro	[13]
21	Jeffrey C. Schlimmer	[3] [4] [5]
22	Man-Lai Tang	[17] [18]
23	Guo-Liang Tian	[15] [17] [18] [19]
24	Hao Xu	[11] [12]
25	Gary Young	[10]
26	Bocheng Zhong	[16]

Colors in the list of coauthors