×

Uh-oh, it looks like your Internet Explorer is out of date.

For a better shopping experience, please upgrade now.

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I / Edition 1
     

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I / Edition 1

by Wray Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor
 

ISBN-10: 3642041795

ISBN-13: 9783642041792

Pub. Date: 09/03/2009

Publisher: Springer Berlin Heidelberg

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

Overview

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.

Product Details

ISBN-13:
9783642041792
Publisher:
Springer Berlin Heidelberg
Publication date:
09/03/2009
Series:
Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence Series , #5781
Edition description:
2009
Pages:
756
Product dimensions:
6.10(w) x 9.10(h) x 1.20(d)

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.

Customer Reviews

Average Review:

Post to your social network

     

Most Helpful Customer Reviews

See all customer reviews