Semi-supervised learning based on kernel methods and graph cut algorithms

Tijl De Bie
Semi-supervised learning based on kernel methods and graph cut algorithms
PhD-thesis, Faculty of Engineering, K.U.Leuven (Leuven, Belgium), May 2005.

pdf

By tijldebie at 07/24/2009 - 10:48

News

Web app could find out if a song has the X Factor
Can Science Predict a Hit Song?
Our work covered in MIT Technology Review
Press release: Scientists map what factors influence the news agenda across the EU

Projects

Statistical foundations of data mining
Music information retrieval
Machine learning
Bioinformatics
Web and text mining

Selected Publications

An end-to-end machine learning system for harmonic analysis of music
An Information Theoretic Framework for Data Mining
Maximum Entropy Models and Subjective Interestingness: an Application to Tiles in Binary Databases
Leveraging Noisy Online Databases for Use in Chord Recognition
Using Online Chord Databases to Enhance Chord Recognition
Refining Causality: Who Copied from Whom?
Mining the Correlation between Lyrical and Audio Features
Interesting Multi-Relational Patterns
Maximum Entropy Modelling for Assessing Results on Real-Valued Data
DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli
MINI: Mining Informative Non-redundant Itemsets

Research homepage - Tijl De Bie

Semi-supervised learning based on kernel methods and graph cut algorithms

News

Projects

Selected Publications