@inproceedings{conf/fuzzIEEE/PirmezDPMR07,
	title = {Applying Fuzzy Logic for Decision-making on Wireless Sensor Networks.},
	author = {Luci Pirmez and Flávia Coimbra Delicato and Paulo F. Pires and Ana L. Mostardinha and Nelson S. de Rezende},
	booktitle = {FUZZ-IEEE},
	crossref = {conf/fuzzIEEE/2007},
	pages = {1-6},
	publisher = {IEEE},
	url = {http://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2007.html#PirmezDPMR07},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2ab80113b2d1e6c5ab2764d27d8d6946a/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1109/FUZZY.2007.4295421}, date = {2008-07-04},
	keywords = {dblp }
}

@inproceedings{conf/mva/LaiY98,
	title = {A Video-based System Methodology for Detecting Red Light Runners.},
	author = {Andrew H. S. Lai and Nelson Hon Ching Yung},
	booktitle = {MVA},
	crossref = {conf/mva/1998},
	pages = {23-26},
	url = {http://dblp.uni-trier.de/db/conf/mva/mva1998.html#LaiY98},
	year = {1998},
	biburl = {http://www.bibsonomy.org/bibtex/2114458816c1b8612a4dc82b300e814c7/dblp},
	description = {dblp},
	ee = {http://b2.cvl.iis.u-tokyo.ac.jp/mva/proceedings/CommemorativeDVD/1998/papers/1998023.pdf}, isbn = {4-901122-98-3}, date = {2008-06-30},
	keywords = {dblp }
}

@inproceedings{conf/icip/Damera-VenkataC07,
	title = {On the Resolution Limits of Superimposed Projection.},
	author = {Niranjan Damera-Venkata and Nelson L. Chang},
	booktitle = {ICIP (5)},
	crossref = {conf/icip/2007},
	pages = {373-376},
	publisher = {IEEE},
	url = {http://dblp.uni-trier.de/db/conf/icip/icip2007-5.html#Damera-VenkataC07},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/27e373eddbe2f8ad3effe43d70e5d4b1a/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1109/ICIP.2007.4379843}, date = {2008-06-26},
	keywords = {dblp }
}

@article{cha89,
	title = {Two-dimensional quantum Heisenberg antiferromagnet at low temperatures},
	author = {Sudip Chakravarty and Bertrand I. Halperin and David R. Nelson},
	journal = {Phys. Rev. B},
	month = {Feb},
	number = {4},
	pages = {2344--2371},
	publisher = {American Physical Society},
	url = {http://dx.doi.org/10.1103/PhysRevB.39.2344},
	volume = {39},
	year = {1989},
	biburl = {http://www.bibsonomy.org/bibtex/28fe4d6b2f1313d7d7313e0475f5303af/jgl},
	posted-at = {2008-03-07 13:36:37}, citeulike-article-id = {2484473}, priority = {2}, doi = {10.1103/PhysRevB.39.2344},
	keywords = {high-tc, htsct, theory }
}

@inproceedings{conf/icip/NganPY07,
	title = {Patterned Fabric Defect Detection using a Motif-Based Approach.},
	author = {Henry Y. T. Ngan and Grantham K. H. Pang and Nelson Hon Ching Yung},
	booktitle = {ICIP (2)},
	crossref = {conf/icip/2007},
	pages = {33-36},
	publisher = {IEEE},
	url = {http://dblp.uni-trier.de/db/conf/icip/icip2007-2.html#NganPY07},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2b1bc2bcf8d9e996847a2749c8394b2a5/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1109/ICIP.2007.4379085}, date = {2008-06-25},
	keywords = {dblp }
}

@inproceedings{conf/icip/MartinsHM07,
	title = {Super-Resolution Image Reconstruction using the ICM Algorithm.},
	author = {Ana Luísa Dine Martins and Murillo R. P. Homem and Nelson D. A. Mascarenhas},
	booktitle = {ICIP (4)},
	crossref = {conf/icip/2007},
	pages = {205-208},
	publisher = {IEEE},
	url = {http://dblp.uni-trier.de/db/conf/icip/icip2007-4.html#MartinsHM07},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2b5d49ea14d4dd4ba9d3e922ce404d865/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1109/ICIP.2007.4379990}, date = {2008-06-25},
	keywords = {dblp }
}

@article{journals/jikm/HruschkaHCE06,
	title = {Bayesian Feature Selection for Clustering Problems.},
	author = {Eduardo R. Hruschka and Estevam R. Hruschka Jr. and Thiago F. Covoes and Nelson F. F. Ebecken},
	journal = {JIKM},
	number = {4},
	pages = {315-327},
	url = {http://dblp.uni-trier.de/db/journals/jikm/jikm5.html#HruschkaHCE06},
	volume = {5},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2936a7f7ab5a14eb1e0c7f3cb84dbe8e5/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1142/S0219649206001578}, date = {2008-06-20},
	keywords = {dblp }
}

@article{ChiZhou:2003:TEC,
	title = {Evolving accurate and compact classification rules
                 with gene expression programming},
	author = {Chi Zhou and Weimin Xiao and Thomas M. Tirpak and Peter C. Nelson},
	journal = {IEEE Transactions on Evolutionary Computation},
	month = {December},
	number = {6},
	pages = {519--531},
	volume = {7},
	year = {2003},
	biburl = {http://www.bibsonomy.org/bibtex/27f7a2dc424e3ec0572027da0cb2ca450/brazovayeye},
	abstract = {Classification is one of the fundamental tasks of data
                 mining. Most rule induction and decision tree
                 algorithms perform local, greedy search to generate
                 classification rules that are often more complex than
                 necessary. Evolutionary algorithms for pattern
                 classification have recently received increased
                 attention because they can perform global searches. In
                 this paper, we propose a new approach for discovering
                 classification rules by using gene expression
                 programming (GEP), a new technique of genetic
                 programming (GP) with linear representation. The
                 antecedent of discovered rules may involve many
                 different combinations of attributes. To guide the
                 search process, we suggest a fitness function
                 considering both the rule consistency gain and
                 completeness. A multiclass classification problem is
                 formulated as multiple two-class problems by using the
                 one-against-all learning method. The covering strategy
                 is applied to learn multiple rules if applicable for
                 each class. Compact rule sets are subsequently evolved
                 using a two-phase pruning method based on the minimum
                 description length (MDL) principle and the integration
                 theory. Our approach is also noise tolerant and able to
                 deal with both numeric and nominal attributes.
                 Experiments with several benchmark data sets have shown
                 up to 20% improvement in validation accuracy, compared
                 with C4.5 algorithms. Furthermore, the proposed GEP
                 approach is more efficient and tends to generate
                 shorter solutions compared with canonical tree-based GP
                 classifiers.},
	issn = {1089-778X}, size = {13 pages},
	keywords = {GEP algorithms, classification data expression gene genetic mining, programming, rule, }
}

@inproceedings{Zhou:2002:ICAI,
	title = {Discovery of Classification Rules by Using Gene
                 Expression Programming},
	address = {Las Vegas, U.S.A.},
	author = {Chi Zhou and Peter C. Nelson and Weimin Xiao and Thomas M. Tirpak},
	booktitle = {Proceedings of the International Conference on
                 Artificial Intelligence (IC-AI'02)},
	month = {June},
	pages = {1355--1361},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/25a153229190d6f8cba8e4fa33ef32d27/brazovayeye},
	keywords = {algorithms, genetic programming }
}

@inproceedings{Zhang:gecco06lbp,
	title = {Using Differential Evolution for {GEP} Constant
                 Creation},
	address = {Seattle, WA, USA},
	author = {Qiongyun Zhang and Chi Zhou and Weimin Xiao and Peter C. Nelson and Xin Li},
	booktitle = {Late breaking paper at Genetic and Evolutionary
                 Computation Conference {(GECCO'2006)}},
	editor = {J{\"{o}}rn Grahl},
	month = {8-12 July},
	url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp130.pdf},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/20d01c5bfb43961426b94abbf3ffbf422/brazovayeye},
	abstract = {Gene Expression Programming (GEP) is a new
                 evolutionary algorithm that incorporates both the idea
                 of simple, linear chromosomes of fixed length used in
                 Genetic Algorithms (GAs) and the structure of different
                 sizes and shapes used in Genetic Programming (GP). As
                 with other genetic programming algorithms, GEP has
                 difficulty finding appropriate numeric constants for
                 terminal nodes in the expression trees. In this paper,
                 we describe a new approach of constant generation using
                 Differential Evolution (DE), which is a simple
                 real-valued GA that has proven to be robust and
                 efficient on parameter optimisation problems. Our
                 experimental results on two symbolic regression
                 problems show that the approach significantly improves
                 the performance of the GEP algorithm. The proposed
                 approach can be easily extended to other Genetic
                 Programming variants.},
	notes = {Distributed on CD-ROM at GECCO-2006},
	keywords = {DE algorithms, expression gene genetic programming, }
}