2008 |
19 | EE | 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 | EE | 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 | EE | 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 | EE | Bocheng Zhong,
Jianghong Han,
Zhaofang Du,
Yuan Ming,
Ming Tan:
Game Based Flow Rate Control for Networks.
ICICIC (1) 2006: 401-404 |
15 | EE | 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 | EE | 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 | EE | Steve Gallant,
Gregory Piatetsky-Shapiro,
Ming Tan:
Value-based data mining and web mining for CRM.
KDD Tutorials 2001 |
2000 |
12 | EE | 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 | EE | 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) |