Machine Learning and Knowledge Discovery in Databases : European Conference, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I / edited by Walter Daelemans, Katharina Morik.

Format:

Book

Contributor:

Daelemans, Walter, editor.

Morik, Katharina, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 5211.

Lecture Notes in Artificial Intelligence ; 5211

Language:

English

Subjects (All):

Artificial intelligence.

Database management.

Information storage and retrieval.

Logic, Symbolic and mathematical.

Algorithms.

Mathematical statistics.

Artificial Intelligence.

Database Management.

Information Storage and Retrieval.

Mathematical Logic and Formal Languages.

Algorithm Analysis and Problem Complexity.

Probability and Statistics in Computer Science.

Local Subjects:

Artificial Intelligence.

Database Management.

Information Storage and Retrieval.

Mathematical Logic and Formal Languages.

Algorithm Analysis and Problem Complexity.

Probability and Statistics in Computer Science.

Physical Description:

1 online resource (XXIV, 692 pages).

Edition:

First edition 2008.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.

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 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 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)

Industrializing Data Mining, Challenges and Perspectives

From Microscopy Images to Models of Cellular Processes

Data Clustering: 50 Years Beyond K-means

Learning Language from Its Perceptual Context

The Role of Hierarchies in Exploratory Data Mining

Machine Learning Journal Abstracts

Rollout Sampling Approximate Policy Iteration

New Closed-Form Bounds on the Partition Function

Large Margin vs. Large Volume in Transductive Learning

Incremental Exemplar Learning Schemes for Classification on Embedded Devices

A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity

A Critical Analysis of Variants of the AUC

Improving Maximum Margin Matrix Factorization

Data Mining and Knowledge Discovery Journal Abstracts

Finding Reliable Subgraphs from Large Probabilistic Graphs

A Space Efficient Solution to the Frequent String Mining Problem for Many Databases

The Boolean Column and Column-Row Matrix Decompositions

SkyGraph: An Algorithm for Important Subgraph Discovery in Relational Graphs

Mining Conjunctive Sequential Patterns

Adequate Condensed Representations of Patterns

Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data

Regular Papers

TOPTMH: Topology Predictor for Transmembrane ?-Helices

Learning to Predict One or More Ranks in Ordinal Regression Tasks

Cascade RSVM in Peer-to-Peer Networks

An Algorithm for Transfer Learning in a Heterogeneous Environment

Minimum-Size Bases of Association Rules

Combining Classifiers through Triplet-Based Belief Functions

An Improved Multi-task Learning Approach with Applications in Medical Diagnosis

Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis

Sequence Labelling SVMs Trained in One Pass

Semi-supervised Classification from Discriminative Random Walks

Learning Bidirectional Similarity for Collaborative Filtering

Bootstrapping Information Extraction from Semi-structured Web Pages

Online Multiagent Learning against Memory Bounded Adversaries

Scalable Feature Selection for Multi-class Problems

Learning Decision Trees for Unbalanced Data

Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities

A Fast Method for Training Linear SVM in the Primal

On the Equivalence of the SMO and MDM Algorithms for SVM Training

Nearest Neighbour Classification with Monotonicity Constraints

Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Hierarchical Distance-Based Conceptual Clustering

Mining Frequent Connected Subgraphs Reducing the Number of Candidates

Unsupervised Riemannian Clustering of Probability Density Functions

Online Manifold Regularization: A New Learning Setting and Empirical Study

A Fast Algorithm to Find Overlapping Communities in Networks

A Case Study in Sequential Pattern Mining for IT-Operational Risk

Tight Optimistic Estimates for Fast Subgroup Discovery

Watch, Listen and Learn: Co-training on Captioned Images and Videos

Parameter Learning in Probabilistic Databases: A Least Squares Approach

Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics

One-Class Classification by Combining Density and Class Probability Estimation

Efficient Frequent Connected Subgraph Mining in Graphs of Bounded Treewidth

Proper Model Selection with Significance Test

A Projection-Based Framework for Classifier Performance Evaluation

Distortion-Free Nonlinear Dimensionality Reduction

Learning with L q? vs L 1-Norm Regularisation with Exponentially Many Irrelevant Features

Catenary Support Vector Machines

Exact and Approximate Inference for Annotating Graphs with Structural SVMs

Extracting Semantic Networks from Text Via Relational Clustering

Ranking the Uniformity of Interval Pairs

Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs

StreamKrimp: Detecting Change in Data Streams.

Other Format:

Printed edition:

ISBN:

978-3-540-87479-9

9783540874799

Access Restriction:

Restricted for use by site license.