This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009.
The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper 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.
Author(s): Shai Ben-David (auth.), Wray Buntine, Marko Grobelnik, Dunja Mladenić, John Shawe-Taylor (eds.)
Series: Lecture Notes in Computer Science 5781 : Lecture Notes in Artificial Intelligence
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2009
Language: English
Pages: 756
Tags: Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Database Management; Information Storage and Retrieval; Information Systems and Communication Service; Pattern Recognition
Front Matter....Pages -
Theory-Practice Interplay in Machine Learning – Emerging Theoretical Challenges....Pages 1-1
Are We There Yet?....Pages 2-2
The Growing Semantic Web....Pages 3-3
Privacy in Web Search Query Log Mining....Pages 4-4
Highly Multilingual News Analysis Applications....Pages 5-5
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification....Pages 6-6
On Structured Output Training: Hard Cases and an Efficient Alternative....Pages 7-7
Sparse Kernel SVMs via Cutting-Plane Training....Pages 8-8
Hybrid Least-Squares Algorithms for Approximate Policy Evaluation....Pages 9-9
A Self-training Approach to Cost Sensitive Uncertainty Sampling....Pages 10-10
Learning Multi-linear Representations of Distributions for Efficient Inference....Pages 11-11
Cost-Sensitive Learning Based on Bregman Divergences....Pages 12-12
RTG: A Recursive Realistic Graph Generator Using Random Typing....Pages 13-28
Taxonomy-Driven Lumping for Sequence Mining....Pages 29-29
On Subgroup Discovery in Numerical Domains....Pages 30-30
Harnessing the Strengths of Anytime Algorithms for Constant Data Streams....Pages 31-31
Identifying the Components....Pages 32-32
Two-Way Analysis of High-Dimensional Collinear Data....Pages 33-33
A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process....Pages 34-34
Evaluation Measures for Multi-class Subgroup Discovery....Pages 35-50
Empirical Study of Relational Learning Algorithms in the Phase Transition Framework....Pages 51-66
Topic Significance Ranking of LDA Generative Models....Pages 67-82
Communication-Efficient Classification in P2P Networks....Pages 83-98
A Generalization of Forward-Backward Algorithm....Pages 99-114
Mining Graph Evolution Rules....Pages 115-130
Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks....Pages 131-146
Adaptive XML Tree Classification on Evolving Data Streams....Pages 147-162
A Condensed Representation of Itemsets for Analyzing Their Evolution over Time....Pages 163-178
Non-redundant Subgroup Discovery Using a Closure System....Pages 179-194
PLSI: The True Fisher Kernel and beyond....Pages 195-210
Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization....Pages 211-226
One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs....Pages 227-243
Conference Mining via Generalized Topic Modeling....Pages 244-259
Within-Network Classification Using Local Structure Similarity....Pages 260-275
Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)....Pages 276-289
Kernel Polytope Faces Pursuit....Pages 290-301
Soft Margin Trees....Pages 302-314
Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs....Pages 315-329
Margin and Radius Based Multiple Kernel Learning....Pages 330-343
Inference and Validation of Networks....Pages 344-358
Binary Decomposition Methods for Multipartite Ranking....Pages 359-374
Leveraging Higher Order Dependencies between Features for Text Classification....Pages 375-390
Syntactic Structural Kernels for Natural Language Interfaces to Databases....Pages 391-406
Active and Semi-supervised Data Domain Description....Pages 407-422
A Matrix Factorization Approach for Integrating Multiple Data Views....Pages 423-438
Transductive Classification via Dual Regularization....Pages 439-454
Stable and Accurate Feature Selection....Pages 455-468
Efficient Sample Reuse in EM-Based Policy Search....Pages 469-484
Applying Electromagnetic Field Theory Concepts to Clustering with Constraints....Pages 485-500
An ℓ 1 Regularization Framework for Optimal Rule Combination....Pages 501-516
A Generic Approach to Topic Models....Pages 517-532
Feature Selection by Transfer Learning with Linear Regularized Models....Pages 533-547
Integrating Logical Reasoning and Probabilistic Chain Graphs....Pages 548-563
Max-Margin Weight Learning for Markov Logic Networks....Pages 564-579
Parameter-Free Hierarchical Co-clustering by n -Ary Splits....Pages 580-595
Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts....Pages 596-611
Minimum Free Energy Principle for Constraint-Based Learning Bayesian Networks....Pages 612-627
Kernel-Based Copula Processes....Pages 628-643
Compositional Models for Reinforcement Learning....Pages 644-659
Feature Selection for Value Function Approximation Using Bayesian Model Selection....Pages 660-675
Learning Preferences with Hidden Common Cause Relations....Pages 676-691
Feature Selection for Density Level-Sets....Pages 692-704
Efficient Multi-start Strategies for Local Search Algorithms....Pages 705-720
Considering Unseen States as Impossible in Factored Reinforcement Learning....Pages 721-735
Relevance Grounding for Planning in Relational Domains....Pages 736-751
Back Matter....Pages -
