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ORCID10th ECML 1998: Chemnitz, Germany
- Claire Nedellec
, Céline Rouveirol:
Machine Learning: ECML-98, 10th European Conference on Machine Learning, Chemnitz, Germany, April 21-23, 1998, Proceedings. Lecture Notes in Computer Science 1398, Springer 1998, ISBN 3-540-64417-2
Invited Papers
- Kenneth A. De Jong:
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 Support 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 - João 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 A. 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 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
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