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Produktbild: Machine Learning and Knowledge Discovery in Databases
Band 5781

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

Aus der Reihe Lecture Notes in Artificial Intelligence

Fr. 138.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.09.2009

Abbildungen

XXIX, 756 p.

Herausgeber

Wray Buntine + weitere

Verlag

Springer Berlin

Seitenzahl

756

Maße (B/H)

15.5/23.5 cm

Gewicht

1171 g

Sprache

Englisch

ISBN

978-3-642-04179-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.09.2009

Abbildungen

XXIX, 756 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

756

Maße (B/H)

15.5/23.5 cm

Gewicht

1171 g

Sprache

Englisch

ISBN

978-3-642-04179-2

Herstelleradresse

Springer-Verlag GmbH
Heidelberger Platz 3
14197 Berlin
Deutschland
Email: sdc-bookservice@springer.com
Url: www.springer.com
Telephone: +49 30 827870
Fax: +49 30 8214091

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  • Produktbild: Machine Learning and Knowledge Discovery in Databases
  • 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 ThousandLogs: 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.