Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I

Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I / edited by Wray Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor.

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 fro...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Buntine, Wray (Editor), Grobelnik, Marko (Editor), Mladenic, Dunja (Editor), Shawe-Taylor, John (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edition:1st ed. 2009.
Series:Lecture Notes in Artificial Intelligence ; 5781
Springer eBook Collection.
Subjects:
Computer communication systems.
Data mining.
Database management.
Artificial intelligence.
Information storage and retrieval.
Computers.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Invited Talks (Abstracts)
  • Theory-Practice Interplay in Machine Learning – Emerging Theoretical Challenges
  • Are We There Yet?
  • The Growing Semantic Web
  • Privacy in Web Search Query Log Mining
  • Highly Multilingual News Analysis Applications
  • Machine Learning Journal Abstracts
  • Combining Instance-Based Learning and Logistic Regression for Multilabel Classification
  • On Structured Output Training: Hard Cases and an Efficient Alternative
  • Sparse Kernel SVMs via Cutting-Plane Training
  • Hybrid Least-Squares Algorithms for Approximate Policy Evaluation
  • A Self-training Approach to Cost Sensitive Uncertainty Sampling
  • Learning Multi-linear Representations of Distributions for Efficient Inference
  • Cost-Sensitive Learning Based on Bregman Divergences
  • Data Mining and Knowledge Discovery Journal Abstracts
  • RTG: A Recursive Realistic Graph Generator Using Random Typing
  • Taxonomy-Driven Lumping for Sequence Mining
  • On Subgroup Discovery in Numerical Domains
  • Harnessing the Strengths of Anytime Algorithms for Constant Data Streams
  • Identifying the Components
  • Two-Way Analysis of High-Dimensional Collinear Data
  • A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process
  • Regular Papers
  • Evaluation Measures for Multi-class Subgroup Discovery
  • Empirical Study of Relational Learning Algorithms in the Phase Transition Framework
  • Topic Significance Ranking of LDA Generative Models
  • Communication-Efficient Classification in P2P Networks
  • A Generalization of Forward-Backward Algorithm
  • Mining Graph Evolution Rules
  • Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
  • Adaptive XML Tree Classification on Evolving Data Streams
  • A Condensed Representation of Itemsets for Analyzing Their Evolution over Time
  • Non-redundant Subgroup Discovery Using a Closure System
  • PLSI: The True Fisher Kernel and beyond
  • Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization
  • One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs
  • Conference Mining via Generalized Topic Modeling
  • Within-Network Classification Using Local Structure Similarity
  • Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)
  • Kernel Polytope Faces Pursuit
  • Soft Margin Trees
  • Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs
  • Margin and Radius Based Multiple Kernel Learning
  • Inference and Validation of Networks
  • Binary Decomposition Methods for Multipartite Ranking
  • Leveraging Higher Order Dependencies between Features for Text Classification
  • Syntactic Structural Kernels for Natural Language Interfaces to Databases
  • Active and Semi-supervised Data Domain Description
  • A Matrix Factorization Approach for Integrating Multiple Data Views
  • Transductive Classification via Dual Regularization
  • Stable and Accurate Feature Selection
  • Efficient Sample Reuse in EM-Based Policy Search
  • Applying Electromagnetic Field Theory Concepts to Clustering with Constraints
  • An ?1 Regularization Framework for Optimal Rule Combination
  • A Generic Approach to Topic Models
  • Feature Selection by Transfer Learning with Linear Regularized Models
  • Integrating Logical Reasoning and Probabilistic Chain Graphs
  • Max-Margin Weight Learning for Markov Logic Networks
  • Parameter-Free Hierarchical Co-clustering by n-Ary Splits
  • Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts
  • Minimum Free Energy Principle for Constraint-Based Learning Bayesian Networks
  • Kernel-Based Copula Processes
  • Compositional Models for Reinforcement Learning
  • Feature Selection for Value Function Approximation Using Bayesian Model Selection
  • Learning Preferences with Hidden Common Cause Relations
  • Feature Selection for Density Level-Sets
  • Efficient Multi-start Strategies for Local Search Algorithms
  • Considering Unseen States as Impossible in Factored Reinforcement Learning
  • Relevance Grounding for Planning in Relational Domains.

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