Multi-Objective Machine Learning 2006

• Mohua Banerjee, Sushmita Mitra, Ashish Anand:
  Feature Selection Using Rough Sets. 3-20
  
• Julia Handl, Joshua D. Knowles:
  Multi-Objective Clustering and Cluster Validation. 21-47
  
• Luiz S. Oliveira, Marisa E. Morita, Robert Sabourin:
  Feature Selection for Ensembles Using the Multi-Objective Optimization Approach. 49-74
  
• Yang Zhang, Peter Rockett:
  Feature Extraction Using Multi-Objective Genetic Programming. 75-99
  
• Jonathan E. Fieldsend:
  Regression Error Characteristic Optimisation of Non-Linear Models. 103-123
  
• Tomonari Furukawa, Chen Jian Ken Lee, John Michopoulos:
  Regularization for Parameter Identification Using Multi-Objective Optimization. 125-149
  
• Antônio de Pádua Braga, Ricardo H. C. Takahashi, Marcelo Azevedo Costa, Roselito de Albuquerque Teixeira:
  Multi-Objective Algorithms for Neural Networks Learning. 151-171
  
• Hirotaka Nakayama, Yeboon Yun:
  Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming. 173-198
  
• Thorsten Suttorp, Christian Igel:
  Multi-Objective Optimization of Support Vector Machines. 199-220
  
• Gary G. Yen:
  Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design. 221-239
  
• DaeEun Kim:
  Minimizing Structural Risk on Decision Tree Classification. 241-260
  
• Ester Bernadó i Mansilla, Xavier Llorà, Ivan Traus:
  Multi-objective Learning Classifier Systems. 261-288
  
• Yaochu Jin, Bernhard Sendhoff, Edgar Körner:
  Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers. 291-312
  
• Urszula Markowska-Kaczmar, Krystyna Mularczyk:
  GA-Based Pareto Optimization for Rule Extraction from Neural Networks. 313-338
  
• Hanli Wang, Sam Kwong, Yaochu Jin, Chi-Ho Tsang:
  Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems. 339-364
  
• Gary G. Yen:
  Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction. 365-383
  
• Shang-Ming Zhou, John Q. Gan:
  Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model. 385-403
  
• Hussein A. Abbass:
  Pareto-Optimal Approaches to Neuro-Ensemble Learning. 407-427
  
• Arjun Chandra, Huanhuan Chen, Xin Yao:
  Trade-Off Between Diversity and Accuracy in Ensemble Generation. 429-464
  
• Nicolás García-Pedrajas:
  Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks. 465-490
  
• Toshiharu Hatanaka, Nobuhiko Kondo, Katsuji Uosaki:
  Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification. 491-505
  
• Hisao Ishibuchi, Yusuke Nojima:
  Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection. 507-530
  
• Richard M. Everson, Jonathan E. Fieldsend:
  Multi-Objective Optimisation for Receiver Operating Characteristic Analysis. 533-556
  
• Naoyuki Kubota:
  Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination. 557-584
  
• J. M. Lucas, H. Martinez, F. Jimenez:
  Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle. 585-600
  
• María Luque, Oscar Cordón, Enrique Herrera-Viedma:
  A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments. 601-627
  
• Stefan Roth, Alexander Gepperth, Christian Igel:
  Multi-Objective Neural Network Optimization for Visual Object Detection. 629-655