FSCS 2006 Symposium on Fuzzy Systems in Computer Science 2006

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[3]  D. Mackay,et al.  Bayesian methods for adaptive models , 1992 .

[4]  Aljoscha Alexander Klose,et al.  Partially supervised learning of fuzzy classification rules , 2004 .

[5]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[6]  Moninder Singh,et al.  Construction of Bayesian network structures from data: A brief survey and an efficient algorithm , 1995, Int. J. Approx. Reason..

[7]  Einar Broese,et al.  Artificial Neural Networks , 2005 .

[8]  Jesús Alcalá-Fdez,et al.  A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection , 2007, IEEE Transactions on Fuzzy Systems.

[9]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[10]  P. Good.  Resampling Methods , 1999, Birkhäuser Boston.

[11]  F. Herrera,et al.  A proposal on reasoning methods in fuzzy rule-based classification systems , 1999 .

[12]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[13]  Henrik Jacobsson,et al.  Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review , 2005, Neural Computation.

[14]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[15]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[16]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

[17]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[18]  José Salvador Sánchez,et al.  Strategies for learning in class imbalance problems , 2003, Pattern Recognit..

[19]  Kai Heesche,et al.  A Neuro-Fuzzy Approach for the Sensor-Based Driving Situation Identification , 1997, ICONIP.

[20]  Ferenc Szeifert,et al.  Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization , 2003, Int. J. Approx. Reason..

[21]  Eyke Hüllermeier.  Special issue on fuzzy sets in knowledge discovery , 2005, Fuzzy Sets Syst..

[22]  R. Casey,et al.  Advances in Pattern Recognition , 1971 .

[23]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[24]  Mark Craven,et al.  Extracting comprehensible models from trained neural networks , 1996 .

[25]  Detlef Nauck,et al.  Fuzzy-Systeme und Neuro-Fuzzy-Systeme , 1998 .

[26]  Terrence J. Sejnowski,et al.  Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks , 2007 .

[27]  Michael Margaliot,et al.  Are artificial neural networks white boxes? , 2005, IEEE Transactions on Neural Networks.

[28]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[29]  Jenq-Neng Hwang,et al.  Handbook of Neural Network Signal Processing , 2000, IEEE Transactions on Neural Networks.

[30]  Lotfi A. Zadeh,et al.  Toward a generalized theory of uncertainty (GTU)--an outline , 2005, Inf. Sci..

[31]  Rudolf Kruse,et al.  Graphical Models for Industrial Planning on Complex Domains , 2006, Decision Theory and Multi-Agent Planning.

[32]  Marc Schomann.  Wissensorientiertes Performance Measurement , 2001 .

[33]  Bart Kosko,et al.  Adaptive fuzzy systems for backing up a truck-and-trailer , 1992, IEEE Trans. Neural Networks.

[34]  R Wiggins,et al.  Docking a truck: a genetic fuzzy approach , 1992 .

[35]  Rudolf Kruse,et al.  Knwoledge revision in Markov networks , 2004 .

[36]  Yixin Chen,et al.  Support vector learning for fuzzy rule-based classification systems , 2003, IEEE Trans. Fuzzy Syst..

[37]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[38]  E. Polak,et al.  System Theory , 1963 .

[39]  H. Weinfurter,et al.  Experimental quantum teleportation , 1997, Nature.

[40]  Detlef D. Nauck.  Fuzzy data analysis with NEFCLASS , 2003, Int. J. Approx. Reason..

[41]  P. Grangier,et al.  Experimental Realization of Einstein-Podolsky-Rosen-Bohm Gedankenexperiment : A New Violation of Bell's Inequalities , 1982 .

[42]  Detlef Nauck,et al.  SPIDA — A Novel Data Analysis Tool , 2003 .

[43]  Eytan Domany,et al.  Resampling Method for Unsupervised Estimation of Cluster Validity , 2001, Neural Computation.

[44]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[45]  M. V. Geldern.  Strategien erfolgreich umsetzen , 2002 .

[46]  Daniel Sánchez,et al.  Fuzzy association rules: general model and applications , 2003, IEEE Trans. Fuzzy Syst..

[47]  Martin Schlang,et al.  Neural computation in steel industry , 1999, 1999 European Control Conference (ECC).

[48]  Hisashi Shimodaira,et al.  Time-Series Prediction , 2002 .

[49]  Tim Kelly,et al.  Using fuzzy self-organising maps for safety critical systems , 2007, Reliab. Eng. Syst. Saf..

[50]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[51]  Rudolf Kruse,et al.  Obtaining interpretable fuzzy classification rules from medical data , 1999, Artif. Intell. Medicine.

[52]  Francisco Herrera,et al.  A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples , 1997, Int. J. Approx. Reason..

[53]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[54]  Olcay Boz,et al.  Extracting decision trees from trained neural networks , 2002, KDD.

[55]  Detlef Nauck,et al.  Being proactive — analytics for predicting customer actions , 2006 .

[56]  Joachim M. Buhmann,et al.  A Resampling Approach to Cluster Validation , 2002, COMPSTAT.

[57]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[58]  Christian Borgelt,et al.  A framework for discovering interesting business changes from data , 2006 .

[59]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[60]  Tim Kelly,et al.  Safety Lifecycle for Developing Safety Critical Artificial Neural Networks , 2003, SAFECOMP.

[61]  F. Herrera,et al.  Accuracy Improvements in Linguistic Fuzzy Modeling , 2003 .

[62]  Chin-Teng Lin,et al.  Support-vector-based fuzzy neural network for pattern classification , 2006, IEEE Transactions on Fuzzy Systems.

[63]  Anton Schwaighofer,et al.  Transductive and Inductive Methods for Approximate Gaussian Process Regression , 2002, NIPS.

[64]  Shlomo Nir,et al.  NATO ASI Series , 1995 .

[65]  A. Nurnberger,et al.  Improving naive Bayes classifiers using neuro-fuzzy learning , 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

[66]  Brian J. Taylor,et al.  Methods and Procedures for the Verification and Validation of Artificial Neural Networks , 2005 .

[67]  Dieter Zöbel.  Trajectory segmentation for the autonomous control of backward motion for truck and trailer , 2003, IEEE Trans. Intell. Transp. Syst..

[68]  Anil K. Jain,et al.  Bootstrap technique in cluster analysis , 1987, Pattern Recognit..

[69]  Detlef D. Nauck,et al.  Detecting temporally redundant association rules , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[70]  Christian Borgelt,et al.  Graphical models - methods for data analysis and mining , 2002 .

[71]  Christopher M. Bishop,et al.  Neural networks and machine learning , 1998 .

[72]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[73]  J. C. Peters,et al.  Fuzzy Cluster Analysis : A New Method to Predict Future Cardiac Events in Patients With Positive Stress Tests , 1998 .

[74]  Rudolf Kruse,et al.  Parallel Combination of Information Sources , 1998 .

[75]  Hisao Ishibuchi,et al.  Effect of rule weights in fuzzy rule-based classification systems , 2001, IEEE Trans. Fuzzy Syst..

[76]  Charles A. Desoer,et al.  Linear System Theory: The State Space Approach , 2008 .

[77]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[78]  Haym Hirsh,et al.  A Quantitative Study of Small Disjuncts , 2000, AAAI/IAAI.

[79]  Francisco Herrera,et al.  Rule Base Reduction and Genetic Tuning of Fuzzy Systems Based on the Linguistic 3-tuples Representation , 2006, Soft Comput..

[80]  G. Roger,et al.  Experimental Test of Bell's Inequalities Using Time- Varying Analyzers , 1982 .

[81]  D. Mackay,et al.  Introduction to Gaussian processes , 1998 .

[82]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[83]  R. R. Zakrzewski.  Verification of a trained neural network accuracy , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[84]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[85]  Francisco Herrera,et al.  Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques , 2004, Applied Intelligence.