Machine Learning and Knowledge Discovery in Databases

by Daelemans, Walter; Goethals, Bart; Morik, Katharina

• ISBN13: 9783540874782
• ISBN10: 354087478X
• Format: Paperback
• Copyright: 2008-10-01
• Publisher: Springer-Verlag New York Inc

Summary

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Table of Contents

Invited Talks (Abstracts)
Industrializing Data Mining, Challenges and Perspectives	p. 1
From Microscopy Images to Models of Cellular Processes	p. 2
Data Clustering: 50 Years Beyond K-means	p. 3
Learning Language from Its Perceptual Context	p. 5
The Role of Hierarchies in Exploratory Data Mining	p. 6
Machine Learning Journal Abstracts
Rollout Sampling Approximate Policy Iteration	p. 7
New Closed-Form Bounds on the Partition Function	p. 8
Large Margin vs. Large Volume in Transductive Learning	p. 9
Incremental Exemplar Learning Schemes for Classification on Embedded Devices	p. 11
A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity	p. 12
A Critical Analysis of Variants of the AUC	p. 13
Improving Maximum Margin Matrix Factorization	p. 14
Data Mining and Knowledge Discovery Journal Abstracts
Finding Reliable Subgraphs from Large Probabilistic Graphs	p. 15
A Space Efficient Solution to the Frequent String Mining Problem for Many Databases	p. 16
The Boolean Column and Column-Row Matrix Decompositions	p. 17
SkyGraph: An Algorithm for Important Subgraph Discovery in Relational Graphs	p. 18
Mining Conjunctive Sequential Patterns	p. 19
Adequate Condensed Representations of Patterns	p. 20
Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data	p. 22
Regular Papers
TOPTMH: Topology Predictor for Transmembrane [alpha]-Helices	p. 23
Learning to Predict One or More Ranks in Ordinal Regression Tasks	p. 39
Cascade RSVM in Peer-to-Peer Networks	p. 55
An Algorithm for Transfer Learning in a Heterogeneous Environment	p. 71
Minimum-Size Bases of Association Rules	p. 86
Combining Classifiers through Triplet-Based Belief Functions	p. 102
An Improved Multi-task Learning Approach with Applications in Medical Diagnosis	p. 117
Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis	p. 133
Sequence Labelling SVMs Trained in One Pass	p. 146
Semi-supervised Classification from Discriminative Random Walks	p. 162
Learning Bidirectional Similarity for Collaborative Filtering	p. 178
Bootstrapping Information Extraction from Semi-structured Web Pages	p. 195
Online Multiagent Learning against Memory Bounded Adversaries	p. 211
Scalable Feature Selection for Multi-class Problems	p. 227
Learning Decision Trees for Unbalanced Data	p. 241
Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities	p. 257
A Fast Method for Training Linear SVM in the Primal	p. 272
On the Equivalence of the SMO and MDM Algorithms for SVM Training	p. 288
Nearest Neighbour Classification with Monotonicity Constraints	p. 301
Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer	p. 317
Mining Edge-Weighted Call Graphs to Localise Software Bugs	p. 333
Hierarchical Distance-Based Conceptual Clustering	p. 349
Mining Frequent Connected Subgraphs Reducing the Number of Candidates	p. 365
Unsupervised Riemannian Clustering of Probability Density Functions	p. 377
Online Manifold Regularization: A New Learning Setting and Empirical Study	p. 393
A Fast Algorithm to Find Overlapping Communities in Networks	p. 408
A Case Study in Sequential Pattern Mining for IT-Operational Risk	p. 424
Tight Optimistic Estimates for Fast Subgroup Discovery	p. 440
Watch, Listen & Learn: Co-training on Captioned Images and Videos	p. 457
Parameter Learning in Probabilistic Databases: A Least Squares Approach	p. 473
Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics	p. 489
One-Class Classification by Combining Density and Class Probability Estimation	p. 505
Efficient Frequent Connected Subgraph Mining in Graphs of Bounded Treewidth	p. 520
Proper Model Selection with Significance Test	p. 536
A Projection-Based Framework for Classifier Performance Evaluation	p. 548
Distortion-Free Nonlinear Dimensionality Reduction	p. 564
Learning with L[subscript q<1] vs L[subscript 1]-Norm Regularisation with Exponentially Many Irrelevant Features	p. 580
Catenary Support Vector Machines	p. 597
Exact and Approximate Inference for Annotating Graphs with Structural SVMs	p. 611
Extracting Semantic Networks from Text Via Relational Clustering	p. 624
Ranking the Uniformity of Interval Pairs	p. 640
Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs	p. 656
StreamKrimp: Detecting Change in Data Streams	p. 672
Author Index	p. 689
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