ECML-PKDD 2001

55 papers

A Framework for Learning Rules from Multiple Instance Data Yann Chevaleyre, Jean-Daniel Zucker

A Language-Based Similarity Measure Lionel Martin, Frédéric Moal

A Mixture Approach to Novelty Detection Using Training Data with Outliers Martin Lauer

A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker Fredrik A. Dahl

A Simple Approach to Ordinal Classification Eibe Frank, Mark Hall

A Unified Framework for Evaluation Metrics in Classification Using Decision Trees Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish

An Axiomatic Approach to Feature Term Generalization Hassan Aït-Kaci, Yutaka Sasaki

An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning Wojciech Kwedlo, Marek Kretowski

Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example Günther Eibl, Karl Peter Pfeiffer

Applying the Bayesian Evidence Framework to \nu -Support Vector Regression Martin H. C. Law, James T. Kwok

Backpropagation in Decision Trees for Regression Victor Medina-Chico, Alberto Suárez, James F. Lutsko

Building Committees by Clustering Models Based on Pairwise Similarity Values Thomas Ragg

Classification on Data with Biased Class Distribution Slobodan Vucetic, Zoran Obradovic

Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining Heikki Mannila

Comparing the Bayes and Typicalness Frameworks Thomas Melluish, Craig Saunders, Ilia Nouretdinov, Volodya Vovk

Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction Branko Kavsek, Nada Lavrac, Anuska Ferligoj

Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences Marcus Hutter

Discovering Admissible Simultaneous Equation Models from Observed Data Takashi Washio, Hiroshi Motoda, Yuji Niwa

Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy Gerhard Widmer

DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning Carlos Eduardo Mariano, Eduardo F. Morales

Efficiently Determining the Starting Sample Size for Progressive Sampling Baohua Gu, Bing Liu, Feifang Hu, Huan Liu

Estimating the Predictive Accuracy of a Classifier Hilan Bensusan, Alexandros Kalousis

Extraction of Recurrent Patterns from Stratified Ordered Trees Jean-Gabriel Ganascia

Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem Marcus Gallagher

Geometric Properties of Naive Bayes in Nominal Domains Huajie Zhang, Charles X. Ling

Importance Sampling Techniques in Neural Detector Training José L. Sanz-González, Diego Andina

Improving Term Extraction by System Combination Using Boosting Jordi Vivaldi, Lluís Màrquez, Horacio Rodríguez

Improving the Robustness and Encoding Complexity of Behavioural Clones Rui Camacho, Pavel Brazdil

Induction of Qualitative Trees Dorian Suc, Ivan Bratko

Iterative Double Clustering for Unsupervised and Semi-Supervised Learning Ran El-Yaniv, Oren Souroujon

Lazy Induction of Descriptions for Relational Case-Based Learning Eva Armengol, Enric Plaza

Learning of Variability for Invariant Statistical Pattern Recognition Daniel Keysers, Wolfgang Macherey, Jörg Dahmen, Hermann Ney

Learning What People (Don't) Want Thomas Hofmann

Learning When to Collaborate Among Learning Agents Santiago Ontañón, Enric Plaza

Learning While Exploring: Bridging the Gaps in the Eligibility Traces Fredrik A. Dahl, Ole Martin Halck

Mining the Web for Synonyms: PMI-IR Versus LSA on TOEFL Peter D. Turney

On the Practice of Branching Program Boosting Tapio Elomaa, Matti Kääriäinen

Proportional K-Interval Discretization for Naive-Bayes Classifiers Ying Yang, Geoffrey I. Webb

Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery Stefan Wrobel

Second Order Features for Maximising Text Classification Performance Bhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk

Social Agents Playing a Periodical Policy Ann Nowé, Johan Parent, Katja Verbeeck

Speeding up Relational Reinforcement Learning Through the Use of an Incremental First Order Decision Tree Learner Kurt Driessens, Jan Ramon, Hendrik Blockeel

Statistification or Mystification? the Need for Statistical Thought in Visual Data Mining Antony Unwin

Support Vectors for Reinforcement Learning Thomas G. Dietterich, Xin Wang

Symbolic Discriminant Analysis for Mining Gene Expression Patterns Jason H. Moore, Joel S. Parker, Lance W. Hahn

Text Categorization Using Transductive Boosting Hirotoshi Taira, Masahiko Haruno

The Evaluation of Predictive Learners: Some Theoretical and Empirical Results Kevin B. Korb, Lucas R. Hope, Michelle J. Hughes

The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery Gerhard Widmer

Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions Marcus Hutter

Understanding Probabilistic Classifiers Ashutosh Garg, Dan Roth

Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error Gabriele Zenobi, Padraig Cunningham

Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery Ljupco Todorovski, Saso Dzeroski

Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing Lappoon R. Tang, Raymond J. Mooney

Using Subclasses to Improve Classification Learning Achim G. Hoffmann, Rex Bing Hung Kwok, Paul Compton

Wrapping Web Information Providers by Transducer Induction Boris Chidlovskii