10. ECML 1998: Chemnitz, Germany

Invited Papers

• Kenneth DeJong:
  Learning in Agent-Oriented Worlds (Abstract). 3
  
• David D. Lewis:
  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. 4-15
  

Regular Papers

Applications of ML

• Nuno Miguel Marques, José Gabriel Pereira Lopes, Carlos Agra Coelho:
  Learning Verbal Transitivity Using LogLinear Models. 19-24
  
• Lluís Màrquez, Horacio Rodríguez:
  Part-of-Speech Tagging Using Decision Trees. 25-36
  
• François Coste, Jacques Nicolas:
  Inference of Finite Automata: Reducing the Search Space with an Ordering of Pairs of States. 37-42
  
• René Schneider:
  Automatic Acquisition of Lexical Knowledge from Sparse and Noisy Data. 43-48
  
• Sylvain Létourneau, Stan Matwin, Fazel Famili:
  A Normalization Method for Contextual Data: Experience from a Large-Scale Application. 49-54
  
• Claude Sammut, Tatjana Zrimec:
  Learning to Classify X-Ray Images Using Relational Learning. 55-60
  
• Saso Dzeroski, Nico Jacobs, Martín Molina, Carlos Moure:
  ILP Experiments in Detecting Traffic Problems. 61-66
  
• Filippo Neri:
  Simulating Children Learning and Explaining Elementary Heat Transfer Phenomena: A Multistrategy System at Work. 67-76
  

Baysesian Networks

• Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
  Bayes Optimal Instance-Based Learning. 77-88
  
• Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald:
  Bayesian and Information-Theories Priors for Bayesian Network Parameters. 89-94
  

Feature Selection

• Dunja Mladenic:
  Feature Subset Selection in Text-Learning. 95-100
  
• Huan Liu, Hiroshi Motoda, Manoranjan Dash:
  A Monotonic Measure for Optimal Feature Selection. 101-106
  

Decision Trees

• Rui Camacho:
  Inducing Models of human Control Skills. 107-118
  
• Hilan Bensusan:
  God Doesn't Always Shave with Occam's Razor - Learning When and How to Prune. 119-124
  
• Luís Torgo:
  Error Estimators for Pruning Regression Trees. 125-130
  
• Jeffrey P. Bradford, Clayton Kunz, Ron Kohavi, Clifford Brunk, Carla E. Brodley:
  Pruning Decision Trees with Misclassification Costs. 131-136
  

Support Vector Learning

• Thorsten Joachims:
  Text Categorization with Suport Vector Machines: Learning with Many Relevant Features. 137-142
  
• Rolf Rossius, Gérard Zenker, Andreas Ittner, Werner Dilger:
  A Short Note About the Application of Polynomial Kernels with Fractional Degree in Support Vector Learning. 143-148
  

Multiple Models for Classification

• Murlikrishna Viswanathan, Geoffrey I. Webb:
  Classification Learning Using All Rules. 149-159
  
• Miguel Moreira, Eddy Mayoraz:
  Improved Pairwise Coupling Classification with Correcting Classifiers. 160-171
  
• Jacek Jelonek, Jerzy Stefanowski:
  Experiments on Solving Multiclass Learning Problems by n²-classifier. 172-177
  
• Joao Gama:
  Combining Classifiers by Constructive Induction. 178-189
  
• Kai Ming Ting, Zijian Zheng:
  Boosting Trees for Cost-Sensitive Classifications. 190-195
  
• Zijian Zheng:
  Naive Bayesian Classifier Committees. 196-207
  
• Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
  Batch Classification with Discrete Finite Mixtures. 208-213
  

Inductive Logic Programming

• Ute Schmid, Fritz Wysotzki:
  Induction of Recursive Program Schemes. 214-225
  
• Henrik Boström:
  Predicate Invention and Learning from Positive Examples Only. 226-237
  
• Dominique Bouthinon, Henry Soldano:
  An Inductive Logic Programming Framework to Learn a Concept from Ambiguous Examples. 238-249
  

Relational Learning

• Mark Craven, Seán Slattery, Kamal Nigam:
  First-Order Learning for Web Mining. 250-255
  
• Stefan Schrödl:
  Explanation-Based Generalization in Game Playing: Quantitative Results. 256-267
  

Instance Based Learning

• Nicolas Lachiche, Pierre Marquis:
  Scope Classification: An Instance-Based Learning Algorithm with a Rule-Based Characterisation. 268-279
  
• Francesco Ricci, David W. Aha:
  Error-Correcting Output Codes for Local Learners. 280-291
  
• Gianluca Bontempi, Mauro Birattari, Hugues Bersini:
  Recursive Lazy Learning for Modeling and Control. 292-303
  
• Engelbert Mephu Nguifo, Patrick Njiwoua:
  Using Lattice-Based Framework as a Tool for Feature Extraction. 304-309
  

Clustering

• João José Furtado Vasco:
  Determining Property Relevance in Concept Formation by Computing Correlation Between Properties. 310-315
  
• Luis Talavera, Josep Roure:
  A Buffering Strategy to Avoid Ordering Effects in Clustering. 316-321
  

Genetic Algorithms

• Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bello, Lorenza Saitta:
  Coevolutionary, Distributed Search for Inducing Concept Description. 322-333
  
• Antoine Ducoulombier, Michèle Sebag:
  Continuous Mimetic Evolution. 334-345
  
• Björn Olsson:
  A Host-Parasite Genetic Algorithm for Asymmetric Tasks. 346-351
  

Reinforcement Learning

• Marco Wiering, Jürgen Schmidhuber:
  Speeding up Q(lambda)-Learning. 352-363
  
• Antonella Giani, Andrea Sticca, Fabrizio Baiardi, Antonina Starita:
  Q-Learning and Redundancy Reduction in Classifier Systems with Internal State. 364-369
  
• Chris Drummond:
  Composing Functions to Speed up Reinforcement Learning in a Changing World. 370-381
  
• Doina Precup, Richard S. Sutton, Satinder P. Singh:
  Theoretical Results on Reinforcement Learning with Temporally Abstract Options. 382-393
  
• Rémi Munos:
  A General Convergence Method for Reinforcement Learning in the Continuous Case. 394-405
  

Neural Networks

• Ton Weijters, Antal van den Bosch, H. Jaap van den Herik:
  Interpretable Neural Networks with BP-SOM. 406-411
  
• Marghny H. Mohamed, Teruya Minamoto, Koichi Niijima:
  Convergence Rate of Minimization Learning for Neural Networks. 412-417