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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Buntine, Wray (Επιμελητής έκδοσης), Grobelnik, Marko (Επιμελητής έκδοσης), Mladenić, Dunja (Επιμελητής έκδοσης), Shawe-Taylor, John (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
• Σειρά: Lecture Notes in Computer Science, 5781
• Θέματα: Computer science.; Computer communication systems.; Computers.; Database management.; Data mining.; Information storage and retrieval.; Artificial intelligence.; Computer Science.; Computer Communication Networks.; Data Mining and Knowledge Discovery.; Database Management.; Artificial Intelligence (incl. Robotics).; Information Storage and Retrieval.; Information Systems and Communication Service.

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...

Πίνακας περιεχομένων:

• 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.