Machine learning

My work in the area of machine learning focuses on novel learning settings such as semi-supervised learning and structured prediction as well as (kernel-based) data fusion. A lot of my work rests on convex optimization techniques and spectral methods.

Publications: 
Title Authors Status Year
An end-to-end machine learning system for harmonic analysis of music Ni Yizhao, Matt McVicar, Raul Santos-Rodriguez, Tijl De Bie journal paper 2012
Learning to Translate: A Statistical and Computational Analysis Marco Turchi, Tijl De Bie, Cyril Goutte, Nello Cristianini journal paper 2012
Leveraging Noisy Online Databases for Use in Chord Recognition Matt McVicar, Ni Yizhao, Raul Santos-Rodriguez, Tijl De Bie conference paper 2011
Using Online Chord Databases to Enhance Chord Recognition Matt McVicar, Ni Yizhao, Raul Santos-Rodriguez, Tijl De Bie journal paper 2011
Mining the Correlation between Lyrical and Audio Features Matt McVicar, Tim Freeman, Tijl De Bie conference paper 2011
Enhancing Chord Recognition Accuracy using Web Resources Matt McVicar, Tijl De Bie conference paper 2010
Machine Learning with Labeled and Unlabeled Data Tijl De Bie, Thiago Turchetti Maia, Antonio Braga conference paper 2009
Integrating Microarray and Proteomics Data to Predict the Response of Cetuximab in Patients with Rectal Cancer Anneleen Daemen, Olivier Gevaert, Tijl De Bie, Annelies Debucquoy, Jean-Pascal Machiels, Bart De Moor, Karin Haustermans conference paper 2008
Magic Moments for Structured Output Prediction Elisa Ricci, Tijl De Bie, Nello Cristianini journal paper 2008
Learning Performance of a Machine Translation System: a Statistical and Computational Analysis Marco Turchi, Tijl De Bie, Nello Cristianini conference paper 2008
A Metamorphosis of Canonical Correlation Analysis into Multivariate Maximum Margin Learning Sandor Szedmak, Tijl De Bie, David Hardoon conference paper 2007
Deploying SDP for Machine Learning Tijl De Bie conference paper 2007
Learning to Align: a Statistical Approach Elisa Ricci , Tijl De Bie, Nello Cristianini conference paper 2007
Discriminative Sequence Labeling by Z-score Optimization Elisa Ricci, Tijl De Bie, Nello Cristianini conference paper 2007
Modeling Sequence Evolution with Kernel Methods Margherita Bresco, Marco Turchi, Tijl De Bie, Nello Cristianini journal paper 2007
Kernel-Based Data Fusion for Gene Prioritization Tijl De Bie, Leon-Charles Tranchevent, Liesbeth van Oeffelen, Yves Moreau journal paper 2007
The Minimum volume covering ellipsoid estimation in kernel-defined feature spaces Alexander Dolia, Tijl De Bie, Chris Harris, John Shawe-Taylor, Mike Titterington conference paper 2006
Semi-supervised learning using semi-definite programming Tijl De Bie, Nello Cristianini book chapter 2006
Fast SDP relaxations of graph cut clustering, transduction, and other combinatorial problems Tijl De Bie, Nello Cristianini journal paper 2006
Eigenproblems in Pattern Recognition Tijl De Bie, Nello Cristianini, Roman Rosipal book chapter 2005
Semi-supervised learning based on kernel methods and graph cut algorithms Tijl De Bie phd thesis 2005
Kernel methods for exploratory data analysis: a demonstration on text data Tijl De Bie, Nello Cristianini conference paper 2004
Learning from General Label Constraints Tijl De Bie, Johan Suykens, Bart De Moor conference paper 2004
A Statistical Framework for Genomic Data Fusion Gert Lanckriet, Tijl De Bie, Nello Cristianini, Michael Jordan, William Stafford Noble journal paper 2004
Convex Methods for Transduction Tijl De Bie, Nello Cristianini conference paper 2003
On the Regularization of Canonical Correlation Analysis Tijl De Bie, Bart De Moor conference paper 2003
Efficiently Learning the Metric using Side-Information Tijl De Bie, Michinari Momma, Nello Cristianini conference paper 2003