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ORCIDRobert F. Lax
- > Home > Persons > Robert F. Lax
Publications
- 2010
[j7]Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. Chen, Brian D. Marx:
Transforms of pseudo-Boolean random variables. Discret. Appl. Math. 158(1): 13-24 (2010)- 2009
- [c7]Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. Chen, Brian D. Marx:
Learning Terrorist Profiles by Optimization with Two Objectives. IKE 2009: 364-369 - 2008
- [j6]Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. Chen:
Formulas for approximating pseudo-Boolean random variables. Discret. Appl. Math. 156(10): 1581-1597 (2008) - [c6]Jianhua Chen, Jian Xu, Peter P. Chen, Guoli Ding, Robert F. Lax, Brian D. Marx:
Fuzzy Clustering and Iterative Relational Classification for Terrorist Profiling. GrC 2008: 142-147 - [c5]K. Nunez, Jianhua Chen, Peter P. Chen, Guoli Ding, Robert F. Lax, Brian D. Marx:
Empirical Comparison of Greedy Strategies for Learning Markov Networks of Treewidth k. ICMLA 2008: 106-111 - [c4]Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. Chen, Brian D. Marx:
Local Soft Belief Updating for Relational Classification. ISMIS 2008: 525-534 - 2007
- [j5]Guoli Ding, Jianhua Chen, Robert F. Lax, Peter P. Chen:
Graph-theoretic method for merging security system specifications. Inf. Sci. 177(10): 2152-2166 (2007) - [j4]Guoli Ding, Robert F. Lax, Peter P. Chen, Jianhua Chen:
Asymptotic Behavior of Linear Approximations of Pseudo-Boolean Functions. J. Adv. Comput. Intell. Intell. Informatics 11(4): 403-409 (2007) - [c3]Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. Chen, Brian D. Marx:
Comparison of Greedy Strategies for Learning Markov Networks of Treewidth k. MLMTA 2007: 294- - 2005
- [j3]Steven S. Seiden, Peter P. Chen, Robert F. Lax, Jianhua Chen, Guoli Ding:
New bounds for randomized busing. Theor. Comput. Sci. 332(1-3): 63-81 (2005) - [c2]Robert F. Lax, Guoli Ding, Peter P. Chen, Jianhua Chen:
Approximating Pseudo-Boolean Functions on Non-Uniform Domains. IJCAI 2005: 1754-1755 - [c1]Guoli Ding, Jianhua Chen, Robert F. Lax, Peter P. Chen:
Efficient Learning of Pseudo-Boolean Functions from Limited Training Data. ISMIS 2005: 323-331
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