## Machine Learning: ECML 2004: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, ProceedingsJean-Francois Boulicaut, Floriana Esposito, Fosca Giannotti, Dino Pedreschi This book constitutes the refereed proceedings of the 15th European Conference on Machine Learning, ECML 2004, held in Pisa, Italy, in September 2004, jointly with PKDD 2004. The 45 revised full papers and 6 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 280 papers submitted to ECML and 107 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning. |

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Page 124

with true positive and false positive rates. A rule that covers p out of a total of P

PN-space with the co-ordinates (n,p). The covering or separate-and-conquer ...

with true positive and false positive rates. A rule that covers p out of a total of P

**positive examples**and n out of N negative examples is represented as a point inPN-space with the co-ordinates (n,p). The covering or separate-and-conquer ...

Page 130

Below this line (shown dashed in Figure 6), the function is monotonically

increasing (as above), but above this line it starts to decrease again. Thus, for

skewed class distributions with many

where a rule ...

Below this line (shown dashed in Figure 6), the function is monotonically

increasing (as above), but above this line it starts to decrease again. Thus, for

skewed class distributions with many

**positive examples**, there might be caseswhere a rule ...

Page 527

From the view of machine learning, such a user query is a labeled

respectively denote the sets of labeled

.

From the view of machine learning, such a user query is a labeled

**positive****example**, while the image database is a ... data set, £ = V U Af where V and Afrespectively denote the sets of labeled

**positive examples**and negative examples.

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### Contents

Invited Papers | 1 |

RealWorld Learning with Markov Logic Networks | 17 |

Applying Support Vector Machines to Imbalanced Datasets | 39 |

Copyright | |

25 other sections not shown

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### Common terms and phrases

accuracy agent analysis applied approach Artificial Intelligence attributes average Bayesian network Berlin Heidelberg 2004 Boulicaut CITree classification clusters combination compute concept Conference on Machine convergence criterion cross-validation curve Data Mining database dataset decision trees defined denotes distribution domain dynamic ECML ECTD equation error evaluation experiments filter Fisher kernels function approximators graph heuristic hidden Markov models hyperplane improve induced input International Conference iteration kernel learner learning algorithms linear LNAI Machine Learning matrix measure messages method naive Bayes Neural node obtained one-class SVMs optimal overfitting pairs paper parameters performance polynomial positive examples prediction probability estimates problem Q-learning random random forests ranking regression reinforcement learning representation rule sample Section semi-supervised learning Springer-Verlag Berlin Heidelberg Ssair statistics supervised learning support vector machines Table task threshold tion training data training examples training set unlabeled value function variables