The Resource Machine learning : ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001 : proceedings, Luc De Raedt, Peter Flach, eds

Machine learning : ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001 : proceedings, Luc De Raedt, Peter Flach, eds

Label
Machine learning : ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001 : proceedings
Title
Machine learning
Title remainder
ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001 : proceedings
Statement of responsibility
Luc De Raedt, Peter Flach, eds
Creator
Contributor
Subject
Genre
Language
eng
Member of
Cataloging source
DLC
Index
index present
LC call number
Q325.5
LC item number
.E85 2001
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
2001
http://bibfra.me/vocab/lite/meetingName
European Conference on Machine Learning
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorDate
  • 1964-
  • 1956-
http://library.link/vocab/relatedWorkOrContributorName
  • Raedt, Luc de
  • Flach, Peter
Series statement
  • Lecture notes in artificial intelligence
  • Lecture notes in computer science
Series volume
2167.
http://library.link/vocab/subjectName
  • Machine learning
  • Machine learning
  • fouille donnée
  • arbre décision
  • apprentissage base-cas
  • inférence inductive
  • algorithmique
  • apprentissage machine
  • Apprentissage automatique
  • Apprentissage automatique
  • Machine learning
  • Machine learning
  • Machine-learning
  • Leerpsychologie
  • Apprentissage automatique
  • Apprentissage automatique
  • Kongress
  • Maschinelles Lernen
Label
Machine learning : ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001 : proceedings, Luc De Raedt, Peter Flach, eds
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
An Axiomatic Approach to Feature Term Generalization / Hassan Ait-Kaci and Yutaka Sasaki -- Lazy Induction of Descriptions for Relational Case-Based Learning / Eva Armengol and Enric Plaza -- Estimating the Predictive Accuracy of a Classifier / Hilan Bensusan and Alexandros Kalousis -- Improving the Robustness and Encoding Complexity of Behavioural Clones / Rui Camacho and Pavel Brazdil -- A Framework for Learning Rules from Multiple Instance Data / Yann Chevaleyre and Jean-Daniel Zucker -- Wrapping Web Information Providers by Transducer Induction / Boris Chidlovskii -- Learning While Exploring: Bridging the Gaps in the Eligibility Traces / Fredrik A. Dahl and Ole Martin Halck -- A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker / Fredrik A. Dahl -- Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner / Kurt Driessens, Jan Ramon and Hendrik Blockeel -- Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example / Gunther Eibl and Karl Peter Pfeiffer -- Iterative Double Clustering for Unsupervised and Semi-supervised Learning / Ran El-Yaniv and Oren Souroujon -- On the Practice of Branching Program Boosting / Tapio Elomaa and Matti Kaariainen -- A Simple Approach to Ordinal Classification / Eibe Frank and Mark Hall -- Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem / Marcus Gallagher -- Extraction of Recurrent Patterns from Stratified Ordered Trees / Jean-Gabriel Ganascia -- Understanding Probabilistic Classifiers / Ashutosh Garg and Dan Roth -- Efficiently Determining the Starting Sample Size for Progressive Sampling / Baohua Gu, Bing Liu and Feifang Hu / [et al.] -- Using Subclasses to Improve Classification Learning / Achim Hoffmann, Rex Kwok and Paul Compton -- Learning What People (Don't) Want / Thomas Hofmann -- Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions / Marcus Hutter -- Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences / Marcus Hutter -- Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction / Branko Kavsek, Nada Lavrac and Anuska Ferligoj -- Learning of Variability for Invariant Statistical Pattern Recognition / Daniel Keysers, Wolfgang Macherey and Jorg Dahmen / [et al.] -- The Evaluation of Predictive Learners: Some Theoretical and Empirical Results / Kevin B. Korb, Lucas R. Hope and Michelle J. Hughes -- An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning / Wojciech Kwedlo and Marek Kretowski -- A Mixture Approach to Novelty Detection Using Training Data with Outliers / Martin Lauer -- Applying the Bayesian Evidence Framework to v-Support Vector Regression / Martin H. Law and James T. Kwok -- DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning / Carlos E. Mariano and Eduardo F. Morales -- A Language-Based Similarity Measure / Lionel Martin and Frederic Moal -- Backpropagation in Decision Trees for Regression / Victor Medina-Chico, Alberto Suarez and James F. Lutsko -- Comparing the Bayes and Typicalness Frameworks / Thomas Melluish, Craig Saunders and Ilia Nouretdinov / [et al.] -- Symbolic Discriminant Analysis for Mining Gene Expression Patterns / Jason H. Moore, Joel S. Parker and Lance W. Hahn -- Social Agents Playing a Periodical Policy / Ann Nowe, Johan Parent and Katja Verbeeck -- Learning When to Collaborate among Learning Agents / Santiago Ontanon and Enric Plaza -- Building Committees by Clustering Models Based on Pairwise Similarity Values / Thomas Ragg -- Second Order Features for Maximising Text Classification Performance / Bhavani Raskutti, Herman Ferra and Adam Kowalczyk -- Importance Sampling Techniques in Neural Detector Training / Jose L. Sanz-Gonzalez and Diego Andina -- Induction of Qualitative Trees / Dorian Suc and Ivan Bratko -- Text Categorization Using Transductive Boosting / Hirotoshi Taira and Masahiko Haruno -- Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing / Lappoon R. Tang and Raymond J. Mooney -- Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery / Ljupco Todorovski and Saso Dzeroski -- Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL / Peter D. Turney -- A Unified Framework for Evaluation Metrics in Classification Using Decision Trees / Ricardo Vilalta, Mark Brodie and Daniel Oblinger / [et al.] -- Improving Term Extraction by System Combination Using Boosting / Jordi Vivaldi, Lluis Marquez and Horacio Rodriguez -- Classification on Data with Biased Class Distribution / Slobodan Vucetic and Zoran Obradovic -- Discovering Admissible Simultaneous Equation Models from Observed Data / Takashi Washio, Hiroshi Motoda and Yuji Niwa -- Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy / Gerhard Widmer -- Proportional k-Interval Discretization for Naive-Bayes Classifiers / Ying Yang and Geoffrey I. Webb -- Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error / Gabriele Zenobi and Padraig Cunningham -- Geometric Properties of Naive Bayes in Nominal Domains / Huajie Zhang and Charles X. Ling -- Support Vectors for Reinforcement Learning / Thomas G. Dietterich and Xin Wang -- Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining / Heikki Mannila -- Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining / Antony Unwin -- The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery / Gerhard Widmer -- Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery / Stefan Wrobel
Dimensions
24 cm.
Dimensions
unknown
Extent
xvii, 618 pages
Isbn
9783540425366
Lccn
2001044635
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
Specific material designation
remote
System control number
  • (OCoLC)47837927
  • (OCoLC)ocm47837927
Label
Machine learning : ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001 : proceedings, Luc De Raedt, Peter Flach, eds
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
An Axiomatic Approach to Feature Term Generalization / Hassan Ait-Kaci and Yutaka Sasaki -- Lazy Induction of Descriptions for Relational Case-Based Learning / Eva Armengol and Enric Plaza -- Estimating the Predictive Accuracy of a Classifier / Hilan Bensusan and Alexandros Kalousis -- Improving the Robustness and Encoding Complexity of Behavioural Clones / Rui Camacho and Pavel Brazdil -- A Framework for Learning Rules from Multiple Instance Data / Yann Chevaleyre and Jean-Daniel Zucker -- Wrapping Web Information Providers by Transducer Induction / Boris Chidlovskii -- Learning While Exploring: Bridging the Gaps in the Eligibility Traces / Fredrik A. Dahl and Ole Martin Halck -- A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker / Fredrik A. Dahl -- Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner / Kurt Driessens, Jan Ramon and Hendrik Blockeel -- Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example / Gunther Eibl and Karl Peter Pfeiffer -- Iterative Double Clustering for Unsupervised and Semi-supervised Learning / Ran El-Yaniv and Oren Souroujon -- On the Practice of Branching Program Boosting / Tapio Elomaa and Matti Kaariainen -- A Simple Approach to Ordinal Classification / Eibe Frank and Mark Hall -- Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem / Marcus Gallagher -- Extraction of Recurrent Patterns from Stratified Ordered Trees / Jean-Gabriel Ganascia -- Understanding Probabilistic Classifiers / Ashutosh Garg and Dan Roth -- Efficiently Determining the Starting Sample Size for Progressive Sampling / Baohua Gu, Bing Liu and Feifang Hu / [et al.] -- Using Subclasses to Improve Classification Learning / Achim Hoffmann, Rex Kwok and Paul Compton -- Learning What People (Don't) Want / Thomas Hofmann -- Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions / Marcus Hutter -- Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences / Marcus Hutter -- Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction / Branko Kavsek, Nada Lavrac and Anuska Ferligoj -- Learning of Variability for Invariant Statistical Pattern Recognition / Daniel Keysers, Wolfgang Macherey and Jorg Dahmen / [et al.] -- The Evaluation of Predictive Learners: Some Theoretical and Empirical Results / Kevin B. Korb, Lucas R. Hope and Michelle J. Hughes -- An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning / Wojciech Kwedlo and Marek Kretowski -- A Mixture Approach to Novelty Detection Using Training Data with Outliers / Martin Lauer -- Applying the Bayesian Evidence Framework to v-Support Vector Regression / Martin H. Law and James T. Kwok -- DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning / Carlos E. Mariano and Eduardo F. Morales -- A Language-Based Similarity Measure / Lionel Martin and Frederic Moal -- Backpropagation in Decision Trees for Regression / Victor Medina-Chico, Alberto Suarez and James F. Lutsko -- Comparing the Bayes and Typicalness Frameworks / Thomas Melluish, Craig Saunders and Ilia Nouretdinov / [et al.] -- Symbolic Discriminant Analysis for Mining Gene Expression Patterns / Jason H. Moore, Joel S. Parker and Lance W. Hahn -- Social Agents Playing a Periodical Policy / Ann Nowe, Johan Parent and Katja Verbeeck -- Learning When to Collaborate among Learning Agents / Santiago Ontanon and Enric Plaza -- Building Committees by Clustering Models Based on Pairwise Similarity Values / Thomas Ragg -- Second Order Features for Maximising Text Classification Performance / Bhavani Raskutti, Herman Ferra and Adam Kowalczyk -- Importance Sampling Techniques in Neural Detector Training / Jose L. Sanz-Gonzalez and Diego Andina -- Induction of Qualitative Trees / Dorian Suc and Ivan Bratko -- Text Categorization Using Transductive Boosting / Hirotoshi Taira and Masahiko Haruno -- Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing / Lappoon R. Tang and Raymond J. Mooney -- Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery / Ljupco Todorovski and Saso Dzeroski -- Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL / Peter D. Turney -- A Unified Framework for Evaluation Metrics in Classification Using Decision Trees / Ricardo Vilalta, Mark Brodie and Daniel Oblinger / [et al.] -- Improving Term Extraction by System Combination Using Boosting / Jordi Vivaldi, Lluis Marquez and Horacio Rodriguez -- Classification on Data with Biased Class Distribution / Slobodan Vucetic and Zoran Obradovic -- Discovering Admissible Simultaneous Equation Models from Observed Data / Takashi Washio, Hiroshi Motoda and Yuji Niwa -- Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy / Gerhard Widmer -- Proportional k-Interval Discretization for Naive-Bayes Classifiers / Ying Yang and Geoffrey I. Webb -- Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error / Gabriele Zenobi and Padraig Cunningham -- Geometric Properties of Naive Bayes in Nominal Domains / Huajie Zhang and Charles X. Ling -- Support Vectors for Reinforcement Learning / Thomas G. Dietterich and Xin Wang -- Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining / Heikki Mannila -- Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining / Antony Unwin -- The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery / Gerhard Widmer -- Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery / Stefan Wrobel
Dimensions
24 cm.
Dimensions
unknown
Extent
xvii, 618 pages
Isbn
9783540425366
Lccn
2001044635
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
Specific material designation
remote
System control number
  • (OCoLC)47837927
  • (OCoLC)ocm47837927

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