@inproceedings{conf/mva/KimLHS02, title = {Variational Specular Separation Using Color and Polarization.}, author = {Dae Woong Kim and Stephen Lin and Ki-Sang Hong and Heung-Yeung Shum}, booktitle = {MVA}, crossref = {conf/mva/2002}, pages = {176-179}, url = {http://dblp.uni-trier.de/db/conf/mva/mva2002.html#KimLHS02}, year = {2002}, biburl = {http://www.bibsonomy.org/bibtex/2a4ed822854bec397d809e61e315e11f6/dblp}, description = {dblp}, ee = {http://b2.cvl.iis.u-tokyo.ac.jp/mva/proceedings/CommemorativeDVD/2002/papers/2002176.pdf}, isbn = {4-901122-02-9}, date = {2008-06-30}, keywords = {dblp } } @article{journals/ijac/BokutCS07, title = {Markov and Artin Normal Form Theorem for Braid Groups.}, author = {L. A. Bokut and V. V. Chaynikov and K. P. Shum}, journal = {IJAC}, number = {5/6}, pages = {951-961}, url = {http://dblp.uni-trier.de/db/journals/ijac/ijac17.html#BokutCS07}, volume = {17}, year = {2007}, biburl = {http://www.bibsonomy.org/bibtex/2b7abe819660a21a2d8c213a6e83d60ed/dblp}, description = {dblp}, ee = {http://dx.doi.org/10.1142/S0218196707003950}, date = {2008-06-23}, keywords = {dblp } } @inproceedings{Shum:2006:ICCGI, title = {Learning acyclic decision trees with Functional Dependency Network and {MDL} Genetic Programming}, address = {Bucharest}, author = {Wing-Ho Shum and Kwong-Sak Leung and Man-Leung Wong}, booktitle = {International Multi-Conference on Computing in the Global Information Technology, ICCGI '06}, month = {1-3 August}, pages = {25}, publisher = {IEEE}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/2464049d61e708ccb05728e672ae7eca0/brazovayeye}, abstract = {One objective of data mining is to discover parent-child relationships among a set of variables in the domain. Moreover, showing parents' importance can further help to improve decision makings' quality. Bayesian network (BN) is a useful model for multi-class problems and can illustrate parent-child relationships with no cycle. But it cannot show parents' importance. In contrast, decision trees state parents' importance clearly, for instance, the most important parent is put in the first level. However, decision trees are proposed for single-class problems only, when they are applied to multi-class ones, they are likely to produce cycles representing tautologic. In this paper, we propose to use MDL genetic programming (MDLGP) and functional dependency network (FDN) to learn a set of acyclic decision trees (Shum et al., 2005). The FDN is an extension of BN; it can handle all of discrete, continuous, interval and ordinal values; it guarantees to produce decision trees with no cycle; its learning search space is smaller than decision trees'; and it can represent higher-order relationships among variables. The MDLGP is a robust genetic programming (GP) proposed to learn the FDN. We also propose a method to derive acyclic decision trees from the FDN. The experimental results demonstrate that the proposed method can successfully discover the target decision trees, which have no cycle and have the accurate classification results}, isbn = {0-7695-2690-X}, notes = {Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong}, doi = {doi:10.1109/ICCGI.2006.46}, keywords = {algorithms, genetic programming } } @inproceedings{Shum:2006:CEC, title = {Learning non-overlapping rules {A} method based on Functional Dependency Network and {MDL} Genetic Programming}, address = {Vancouver}, author = {Wing-Ho Shum and Kwong-Sak Leung and Man-Leung Wong}, booktitle = {Proceedings of the 2006 IEEE Congress on Evolutionary Computation}, editor = {Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas}, month = {6-21 July}, pages = {2717--2724}, publisher = {IEEE Press}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/27c7679f0ef4258297365cb6de5d0ca26/brazovayeye}, abstract = {Classification rule is a useful model in data mining. Given variable values, rules classify data items into different classes. Different rule learning algorithms are proposed, like Genetic Algorithm (GA) and Genetic Programming (GP). Rules can also be extracted from Bayesian Network (BN) and decision trees. However, all of them have disadvantages and may fail to get the best results. Both of GA and GP cannot handle cooperation among rules and thus, the learnt rules are likely to have many overlappings, i.e. more than one rules classify the same data items and different rules have different predictions. The conflicts among the rules reduce their understandability and increase their usage difficulty for expert systems. In contrast, rules extracted from BN and decision trees have no overlapping in nature. But BN can handle discrete values only and cannot represent higher-order relationships among variables. Moreover, the search space for decision tree learning is huge and thus, it is difficult to reach the global optimum. In this paper, we propose to use Functional Dependency Network (FDN) and MDL Genetic Programming (MDLGP) to learn a set of non-overlapping classification rules [17]. The FDN is an extension of BN; it can handle all kind of values; it can represent higher-order relationships among variables; and its learning search space is smaller than decision trees'. The experimental results demonstrate that the proposed method can successfully discover the target rules, which have no overlapping and have the highest classification accuracies.}, size = {8 pages}, isbn = {0-7803-9487-9}, notes = {WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D}, keywords = {algorithms, genetic poster programming, } } @inproceedings{conf/icdm/ShumLW05, title = {Learning Functional Dependency Networks Based on Genetic Programming}, address = {Houston, Texas, USA}, author = {Wing-Ho Shum and Kwong-Sak Leung and Man Leung Wong}, booktitle = {Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005)}, month = {27-30 November}, pages = {394--401}, publisher = {IEEE Computer Society}, url = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.86}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/22461bee242feaaf143fb7b19fdff10ad/brazovayeye}, abstract = {Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous, interval and ordinal ones, which must be converted to discrete values and the order information is lost. Thus, BN tends to have higher network complexity and lower understandability. In this paper, we present a novel dependency network which can handle discrete, continuous, interval and ordinal values through functions; it has lower network complexity and stronger expressive power; it can represent any kind of relationships; and it can incorporate a-priori knowledge though user-defined functions. We also propose a novel Genetic Programming (GP) to learn dependency networks. The novel GP does not use any knowledge-guided nor application-oriented operator, thus it is robust and easy to replicate. The experimental results demonstrate that the novel GP can successfully discover the target novel dependency networks, which have the highest accuracy and the lowest network complexity.}, bibsource = {DBLP, http://dblp.uni-trier.de/db/conf/icdm/icdm2005.html#ShumLW05}, bibdate = {2005-12-21}, isbn = {0-7695-2278-5}, keywords = {algorithms, genetic programming } } @inproceedings{conf/ideal/ShumLW05, title = {Co-evolutionary Rule-Chaining Genetic Programming}, address = {Brisbane, Australia}, author = {Wing-Ho Shum and Kwong-Sak Leung and Man Leung Wong}, booktitle = {Intelligent Data Engineering and Automated Learning - IDEAL 2005, 6th International Conference, Proceedings}, editor = {Marcus Gallagher and James M. Hogan and Fr{\'e}d{\'e}ric Maire}, month = {July 6-8}, pages = {546--554}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = {3578}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/2abd1f5d545a13a95e02706db78392283/brazovayeye}, abstract = {Genetic Programming (GP) paradigm called Co-evolutionary Rule-Chaining Genetic Programming (CRGP) has been proposed to learn the relationships among attributes represented by a set of classification rules for multi-class problems. It employs backward chaining inference to carry out classification based on the acquired acyclic rule set. Its main advantages are: 1) it can handle more than one class at a time; 2) it avoids cyclic result; 3) unlike Bayesian Network (BN), the CRGP can handle input attributes with continuous values directly; and 4) with the flexibility of GP, CRGP can learn complex relationship. We have demonstrated its better performance on one synthetic and one real-life medical data sets.}, bibsource = {DBLP, http://dblp.uni-trier.de/db/conf/ideal/ideal2005.html#ShumLW05}, size = {9 pages}, bibdate = {2005-06-23}, isbn = {3-540-26972-X}, notes = {(1) Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong (2) Department of Information Systems, Lingnan University, Tuen Mun, Hong Kong}, doi = {doi:10.1007/11508069_71}, keywords = {Agents Complex Systems algorithms, and genetic programming, } } @inproceedings{conf/comma/Shum08, title = {Cohere: Towards Web 2.0 Argumentation.}, author = {Simon Buckingham Shum}, booktitle = {COMMA}, crossref = {conf/comma/2008}, editor = {Philippe Besnard and Sylvie Doutre and Anthony Hunter}, pages = {97-108}, publisher = {IOS Press}, series = {Frontiers in Artificial Intelligence and Applications}, url = {http://dblp.uni-trier.de/db/conf/comma/comma2008.html#Shum08}, volume = {172}, year = {2008}, biburl = {http://www.bibsonomy.org/bibtex/26edbebc95381d17f5250214c377624bd/dblp}, description = {dblp}, date = {2008-06-15}, isbn = {978-1-58603-859-5}, keywords = {dblp } } @inproceedings{conf/comma/BennSDM08, title = {Ontological Foundations for Scholarly Debate Mapping Technology.}, author = {Neil Benn and Simon Buckingham Shum and John Domingue and Clara Mancini}, booktitle = {COMMA}, crossref = {conf/comma/2008}, editor = {Philippe Besnard and Sylvie Doutre and Anthony Hunter}, pages = {61-72}, publisher = {IOS Press}, series = {Frontiers in Artificial Intelligence and Applications}, url = {http://dblp.uni-trier.de/db/conf/comma/comma2008.html#BennSDM08}, volume = {172}, year = {2008}, biburl = {http://www.bibsonomy.org/bibtex/2bfe0146fc4708320aa742f09af1350d1/dblp}, description = {dblp}, date = {2008-06-15}, isbn = {978-1-58603-859-5}, keywords = {dblp } } @inproceedings{conf/dagstuhl/GumholdK07, title = {Image-Based Motion Compensation for Structured Light Scanning of Dynamic Surfaces.}, author = {Stefan Gumhold and Sören König}, booktitle = {Visual Computing - Convergence of Computer Graphics and Computer Vision}, crossref = {conf/dagstuhl/2007P7171}, editor = {Markus H. Gross and Heinrich Müller and Hans-Peter Seidel and Harry Shum}, publisher = {Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany}, series = {Dagstuhl Seminar Proceedings}, url = {http://dblp.uni-trier.de/db/conf/dagstuhl/P7171.html#GumholdK07}, volume = {07171}, year = {2007}, biburl = {http://www.bibsonomy.org/bibtex/2c430cb2757280266782c51632961d900/dblp}, description = {dblp}, date = {2008-06-12}, ee = {http://drops.dagstuhl.de/opus/volltexte/2008/1502}, keywords = {dblp } } @inproceedings{conf/dagstuhl/GrossMSS07, title = {07171 Summary -- Visual Computing -- Convergence of Computer Graphics and Computer Vision.}, author = {Markus H. Gross and Heinrich Müller and Hans-Peter Seidel and Harry Shum}, booktitle = {Visual Computing - Convergence of Computer Graphics and Computer Vision}, crossref = {conf/dagstuhl/2007P7171}, editor = {Markus H. Gross and Heinrich Müller and Hans-Peter Seidel and Harry Shum}, publisher = {Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany}, series = {Dagstuhl Seminar Proceedings}, url = {http://dblp.uni-trier.de/db/conf/dagstuhl/P7171.html#GrossMSS07}, volume = {07171}, year = {2007}, biburl = {http://www.bibsonomy.org/bibtex/2492fc3b5d84a1e2c7b8177fcb5e15eca/dblp}, description = {dblp}, date = {2008-06-12}, ee = {http://drops.dagstuhl.de/opus/volltexte/2008/1503}, keywords = {dblp } }