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Ming Tan Vis

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*2008
20EEZhenqiu Liu, Ronald B. Gartenhaus, Ming Tan, Feng Jiang, Xiaoli Jiao: Gene and pathway identification with Lp penalized Bayesian logistic regression. BMC Bioinformatics 9: (2008)
19EEGuo-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)
18EEGuo-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
17EEMan-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
16EEBocheng Zhong, Jianghong Han, Zhaofang Du, Yuan Ming, Ming Tan: Game Based Flow Rate Control for Networks. ICICIC (1) 2006: 401-404
15EEMing 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)
14EEZhenqiu 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
13EESteve Gallant, Gregory Piatetsky-Shapiro, Ming Tan: Value-based data mining and web mining for CRM. KDD Tutorials 2001
2000
12EEMing 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
5EEMing 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

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

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)