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.

Format:

Book

Contributor:

Buntine, Wray, editor.

Grobelnik, Marko, editor.

Mladenić, Dunja, 1967- editor.

Shawe-Taylor, John, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 5781.

Lecture Notes in Artificial Intelligence ; 5781

Language:

English

Subjects (All):

Computer networks.

Data mining.

Database management.

Artificial intelligence.

Information storage and retrieval.

Computers.

Computer Communication Networks.

Data Mining and Knowledge Discovery.

Database Management.

Artificial Intelligence.

Information Storage and Retrieval.

Information Systems and Communication Service.

Local Subjects:

Computer Communication Networks.

Data Mining and Knowledge Discovery.

Database Management.

Artificial Intelligence.

Information Storage and Retrieval.

Information Systems and Communication Service.

Physical Description:

1 online resource (XXIX, 756 pages).

Edition:

First edition 2009.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-642-04180-8

9783642041808

Access Restriction:

Restricted for use by site license.