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2020 – today
- 2021
[j32]Junning Deng
, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Mining explainable local and global subgraph patterns with surprising densities. Data Min. Knowl. Discov. 35(1): 321-371 (2021)- [i27]Maarten Buyl, Tijl De Bie:
The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer. CoRR abs/2103.01846 (2021) - 2020
- [j31]Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive visual data exploration with subjective feedback: an information-theoretic approach. Data Min. Knowl. Discov. 34(1): 21-49 (2020) - [j30]Anes Bendimerad, Ahmad Mel, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
SIAS-miner: mining subjectively interesting attributed subgraphs. Data Min. Knowl. Discov. 34(2): 355-393 (2020) - [j29]Florian Adriaens, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, Antonis Matakos, Polina Rozenshtein:
Relaxing the strong triadic closure problem for edge strength inference. Data Min. Knowl. Discov. 34(3): 611-651 (2020) - [j28]Robin Vandaele, Yvan Saeys, Tijl De Bie:
Mining Topological Structure in Graphs through Forest Representations. J. Mach. Learn. Res. 21: 215:1-215:68 (2020) - [j27]Bo Kang, Kai Puolamäki, Jefrey Lijffijt, Tijl De Bie:
A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback. IEEE Trans. Knowl. Data Eng. 32(9): 1666-1679 (2020) - [c74]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. AIES 2020: 344-350 - [c73]Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie:
CSNE: Conditional Signed Network Embedding. CIKM 2020: 1105-1114 - [c72]Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
Block-Approximated Exponential Random Graphs. DSAA 2020: 70-80 - [c71]Alexandru Cristian Mara, Jefrey Lijffijt, Tijl De Bie:
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress? DSAA 2020: 138-147 - [c70]Ahmad Mel, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
FONDUE: Framework for Node Disambiguation Using Network Embeddings. DSAA 2020: 158-167 - [c69]Maarten Buyl, Tijl De Bie:
DeBayes: a Bayesian Method for Debiasing Network Embeddings. ICML 2020: 1220-1229 - [c68]Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
Gibbs Sampling Subjectively Interesting Tiles. IDA 2020: 80-92 - [c67]Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach. SDM 2020: 586-594 - [i26]Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach. CoRR abs/2002.00793 (2020) - [i25]Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
ALPINE: Active Link Prediction using Network Embedding. CoRR abs/2002.01227 (2020) - [i24]Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
Scalable Dyadic Independence Models with Local and Global Constraints. CoRR abs/2002.07076 (2020) - [i23]Ahmad Mel, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
FONDUE: A Framework for Node Disambiguation Using Network Embeddings. CoRR abs/2002.10127 (2020) - [i22]Maarten Buyl, Tijl De Bie:
DeBayes: a Bayesian method for debiasing network embeddings. CoRR abs/2002.11442 (2020) - [i21]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
Network Representation Learning for Link Prediction: Are we improving upon simple heuristics? CoRR abs/2002.11522 (2020) - [i20]Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie:
CSNE: Conditional Signed Network Embedding. CoRR abs/2005.10701 (2020)
2010 – 2019
- 2019
- [j26]Florian Adriaens, Jefrey Lijffijt, Tijl De Bie:
Subjectively interesting connecting trees and forests. Data Min. Knowl. Discov. 33(4): 1088-1124 (2019) - [j25]Junning Deng, Jefrey Lijffijt, Bo Kang, Tijl De Bie:
SIMIT: Subjectively Interesting Motifs in Time Series. Entropy 21(6): 566 (2019) - [j24]Valerio Lorenzoni, Pieter Van den Berghe, Pieter-Jan Maes, Tijl De Bie, Dirk De Clercq, Marc Leman:
Design and validation of an auditory biofeedback system for modification of running parameters. J. Multimodal User Interfaces 13(3): 167-180 (2019) - [c66]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Conditional Network Embeddings. BNAIC/BENELEARN 2019 - [c65]Florian Adriaens, Çigdem Aslay, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt:
Discovering Interesting Cycles in Directed Graphs. CIKM 2019: 1191-1200 - [c64]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Conditional Network Embeddings. ICLR (Poster) 2019 - [c63]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. RML@ICLR 2019 - [c62]Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
Contrastive Antichains in Hierarchies. KDD 2019: 294-304 - [c61]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. EDML@SDM 2019: 5-13 - [i19]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. CoRR abs/1901.09691 (2019) - [i18]Xi Chen, Panayiotis Tsaparas, Jefrey Lijffijt, Tijl De Bie:
Opinion Dynamics with Backfire Effect and Biased Assimilation. CoRR abs/1903.11535 (2019) - [i17]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions. CoRR abs/1904.12694 (2019) - [i16]Anes Bendimerad, Ahmad Mel, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
Mining Subjectively Interesting Attributed Subgraphs. CoRR abs/1905.03040 (2019) - [i15]Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information. CoRR abs/1905.10086 (2019) - [i14]Florian Adriaens, Çigdem Aslay, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt:
Discovering Interesting Cycles in Directed Graphs. CoRR abs/1909.01060 (2019) - [i13]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. CoRR abs/1909.09369 (2019) - 2018
- [j23]Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
SICA: subjectively interesting component analysis. Data Min. Knowl. Discov. 32(4): 949-987 (2018) - [j22]Len Vande Veire, Tijl De Bie:
From raw audio to a seamless mix: creating an automated DJ system for Drum and Bass. EURASIP J. Audio Speech Music. Process. 2018: 13 (2018) - [c60]Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach. ICDE 2018: 1208-1211 - [c59]Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie:
Subjectively Interesting Subgroup Discovery on Real-Valued Targets. ICDE 2018: 1352-1355 - [c58]Xi Chen, Jefrey Lijffijt, Tijl De Bie:
Quantifying and Minimizing Risk of Conflict in Social Networks. KDD 2018: 1197-1205 - [c57]Valerio Lorenzoni, Pieter-Jan Maes, Pieter Van den Berghe, Dirk De Clercq, Tijl De Bie, Marc Leman:
A biofeedback music-sonification system for gait retraining. MOCO 2018: 28:1-28:5 - [c56]Robin Vandaele, Tijl De Bie, Yvan Saeys:
Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies. ECML/PKDD (2) 2018: 19-36 - [c55]Rafael Poyiadzi, Raúl Santos-Rodríguez, Tijl De Bie:
Ordinal Label Proportions. ECML/PKDD (1) 2018: 306-321 - [i12]Florian Adriaens, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, Polina Rozenshtein:
From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths. CoRR abs/1802.03549 (2018) - [i11]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Conditional Network Embeddings. CoRR abs/1805.07544 (2018) - [i10]Tijl De Bie, Luc De Raedt, Holger H. Hoos, Padhraic Smyth:
Automating Data Science (Dagstuhl Seminar 18401). Dagstuhl Reports 8(9): 154-181 (2018) - 2017
- [c54]Paolo Simeone, Raúl Santos-Rodríguez, Matt McVicar, Jefrey Lijffijt, Tijl De Bie:
Hierarchical Novelty Detection. IDA 2017: 310-321 - [c53]Florian Adriaens, Jefrey Lijffijt, Tijl De Bie:
Subjectively Interesting Connecting Trees. ECML/PKDD (2) 2017: 53-69 - [i9]Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie:
Subjectively Interesting Subgroup Discovery on Real-valued Targets. CoRR abs/1710.04521 (2017) - [i8]Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach. CoRR abs/1710.08167 (2017) - 2016
- [j21]Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, Tijl De Bie:
P-N-RMiner: a generic framework for mining interesting structured relational patterns. Int. J. Data Sci. Anal. 1(1): 61-76 (2016) - [j20]Matthijs van Leeuwen, Tijl De Bie, Eirini Spyropoulou, Cédric Mesnage:
Subjective interestingness of subgraph patterns. Mach. Learn. 105(1): 41-75 (2016) - [j19]Matt McVicar, Benjamin Sach, Cédric Mesnage, Jefrey Lijffijt, Eirini Spyropoulou, Tijl De Bie:
SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees. Pattern Recognit. Lett. 79: 52-59 (2016) - [c52]Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodríguez, Bo Kang:
Informative data projections: a framework and two examples. ESANN 2016 - [c51]Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
Learning to separate vocals from polyphonic mixtures via ensemble methods and structured output prediction. ICASSP 2016: 450-454 - [c50]Tias Guns, Achille Aknin, Jefrey Lijffijt, Tijl De Bie:
Direct Mining of Subjectively Interesting Relational Patterns. ICDM 2016: 913-918 - [c49]Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodriguez, Tijl De Bie:
Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations. KDD 2016: 1615-1624 - [c48]Tijl De Bie, Jefrey Lijffijt, Cédric Mesnage, Raúl Santos-Rodriguez:
Detecting trends in twitter time series. MLSP 2016: 1-6 - [c47]Bo Kang, Kai Puolamäki, Jefrey Lijffijt, Tijl De Bie:
A Tool for Subjective and Interactive Visual Data Exploration. ECML/PKDD (3) 2016: 3-7 - [c46]Kai Puolamäki, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive Visual Data Exploration with Subjective Feedback. ECML/PKDD (2) 2016: 214-229 - 2015
- [j18]Kleanthis-Nikolaos Kontonasios, Tijl De Bie:
Subjectively interesting alternative clusterings. Mach. Learn. 98(1-2): 31-56 (2015) - [c45]Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, Tijl De Bie:
P-N-RMiner: A generic framework for mining interesting structured relational patterns. DSAA 2015: 1-10 - [c44]Matt McVicar, Cédric Mesnage, Jefrey Lijffijt, Tijl De Bie:
Interactively Exploring Supply and Demand in the UK Independent Music Scene. ECML/PKDD (3) 2015: 289-292 - [c43]Matt McVicar, Cédric Mesnage, Jefrey Lijffijt, Eirini Spyropoulou, Tijl De Bie:
Supply and demand of independent UK music artists on the web. WebSci 2015: 48:1-48:2 - [e3]Élisa Fromont, Tijl De Bie, Matthijs van Leeuwen:
Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015, Saint Etienne, France, October 22-24, 2015, Proceedings. Lecture Notes in Computer Science 9385, Springer 2015, ISBN 978-3-319-24464-8 [contents] - [i7]Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodriguez, Bo Kang:
Informative Data Projections: A Framework and Two Examples. CoRR abs/1511.08762 (2015) - 2014
- [j17]Eirini Spyropoulou, Tijl De Bie, Mario Boley:
Interesting pattern mining in multi-relational data. Data Min. Knowl. Discov. 28(3): 808-849 (2014) - [j16]Matt McVicar, Raúl Santos-Rodriguez, Yizhao Ni, Tijl De Bie:
Automatic Chord Estimation from Audio: A Review of the State of the Art. IEEE ACM Trans. Audio Speech Lang. Process. 22(2): 556-575 (2014) - [c42]Eirini Spyropoulou, Tijl De Bie:
Mining approximate multi-relational patterns. DSAA 2014: 477-483 - 2013
- [j15]Tijl De Bie, Peter A. Flach:
Guest editors' introduction: special section of selected papers from ECML-PKDD 2012. Data Min. Knowl. Discov. 27(3): 442-443 (2013) - [j14]Tijl De Bie, Peter A. Flach:
Guest editors' introduction: special issue of selected papers from ECML-PKDD 2012. Mach. Learn. 92(1): 1-3 (2013) - [j13]Yizhao Ni, Matt McVicar, Raúl Santos-Rodríguez, Tijl De Bie:
Understanding Effects of Subjectivity in Measuring Chord Estimation Accuracy. IEEE ACM Trans. Audio Speech Lang. Process. 21(12): 2607-2615 (2013) - [c41]Eirini Spyropoulou, Tijl De Bie, Mario Boley:
Mining Interesting Patterns in Multi-relational Data with N-ary Relationships. Discovery Science 2013: 217-232 - [c40]Tijl De Bie:
Subjective Interestingness in Exploratory Data Mining. IDA 2013: 19-31 - [c39]Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, Tijl De Bie:
Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data. ECML/PKDD (2) 2013: 256-271 - [c38]Tijl De Bie, Eirini Spyropoulou:
A Theoretical Framework for Exploratory Data Mining: Recent Insights and Challenges Ahead. ECML/PKDD (3) 2013: 612-616 - 2012
- [j12]Marco Turchi, Tijl De Bie, Cyril Goutte, Nello Cristianini:
Learning to Translate: A Statistical and Computational Analysis. Adv. Artif. Intell. 2012: 484580:1-484580:15 (2012) - [j11]Nick Fyson, Tijl De Bie, Nello Cristianini:
The NetCover algorithm for the reconstruction of causal networks. Neurocomputing 96: 19-28 (2012) - [j10]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
An End-to-End Machine Learning System for Harmonic Analysis of Music. IEEE Trans. Speech Audio Process. 20(6): 1771-1783 (2012) - [j9]Marco Turchi, Tijl De Bie, Nello Cristianini:
An intelligent Web agent that autonomously learns how to translate. Web Intell. Agent Syst. 10(2): 165-178 (2012) - [j8]Kleanthis-Nikolaos Kontonasios, Eirini Spyropoulou, Tijl De Bie:
Knowledge discovery interestingness measures based on unexpectedness. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(5): 386-399 (2012) - [c37]Omar Ali, Giovanni Zappella, Tijl De Bie, Nello Cristianini:
An Empirical Comparison of Label Prediction Algorithms on Automatically Inferred Networks. ICPRAM (2) 2012: 259-268 - [c36]Kleanthis-Nikolaos Kontonasios, Tijl De Bie:
Formalizing Complex Prior Information to Quantify Subjective Interestingness of Frequent Pattern Sets. IDA 2012: 161-171 - [c35]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
Using Hyper-genre Training to Explore Genre Information for Automatic Chord Estimation. ISMIR 2012: 109-114 - [e2]Peter A. Flach, Tijl De Bie, Nello Cristianini:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I. Lecture Notes in Computer Science 7523, Springer 2012, ISBN 978-3-642-33459-7 [contents] - [e1]Peter A. Flach, Tijl De Bie, Nello Cristianini:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II. Lecture Notes in Computer Science 7524, Springer 2012, ISBN 978-3-642-33485-6 [contents] - 2011
- [j7]Tijl De Bie:
Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Min. Knowl. Discov. 23(3): 407-446 (2011) - [c34]Nick Fyson, Tijl De Bie, Nello Cristianini:
Reconstruction of Causal Networks by Set Covering. ICANNGA (2) 2011: 196-205 - [c33]Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, Tijl De Bie:
Maximum Entropy Modelling for Assessing Results on Real-Valued Data. ICDM 2011: 350-359 - [c32]Eirini Spyropoulou, Tijl De Bie:
Interesting Multi-relational Patterns. ICDM 2011: 675-684 - [c31]Matt McVicar, Yizhao Ni, Tijl De Bie, Raúl Santos-Rodriguez:
Leveraging Noisy Online Databases for Use in Chord Recognition. ISMIR 2011: 639-644 - [c30]Matt McVicar, Tim Freeman, Tijl De Bie:
Mining the Correlation between Lyrical and Audio Features and the Emergence of Mood. ISMIR 2011: 783-788 - [c29]Tristan Mark Snowsill, Nick Fyson, Tijl De Bie, Nello Cristianini:
Refining causality: who copied from whom? KDD 2011: 466-474 - [c28]Tijl De Bie:
An information theoretic framework for data mining. KDD 2011: 564-572 - [c27]Tijl De Bie:
Subjectively Interesting Alternative Clusters. MultiClust@ECML/PKDD 2011: 43-54 - [c26]Omar Ali, Ilias N. Flaounas, Tijl De Bie, Nello Cristianini:
Celebrity Watch: Browsing News Content by Exploiting Social Intelligence. ECML/PKDD (3) 2011: 613-616 - [c25]Ilias N. Flaounas, Omar Ali, Marco Turchi, Tristan Snowsill, Florent Nicart, Tijl De Bie, Nello Cristianini:
NOAM: news outlets analysis and monitoring system. SIGMOD Conference 2011: 1275-1278 - [i6]Eirini Spyropoulou, Tijl De Bie:
Interesting Multi-Relational Patterns. CoRR abs/1106.4475 (2011) - [i5]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
An end-to-end machine learning system for harmonic analysis of music. CoRR abs/1107.4969 (2011) - [i4]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
Meta-song evaluation for chord recognition. CoRR abs/1109.0420 (2011) - 2010
- [j6]Tijl De Bie, Kleanthis-Nikolaos Kontonasios, Eirini Spyropoulou:
A framework for mining interesting pattern sets. SIGKDD Explor. 12(2): 92-100 (2010) - [c24]Tristan Snowsill, Florent Nicart, Marco Stefani, Tijl De Bie, Nello Cristianini:
Finding surprising patterns in textual data streams. CIP 2010: 405-410 - [c23]Vasileios Lampos, Tijl De Bie, Nello Cristianini:
Flu Detector - Tracking Epidemics on Twitter. ECML/PKDD (3) 2010: 599-602 - [c22]Tristan Snowsill, Ilias N. Flaounas, Tijl De Bie, Nello Cristianini:
Detecting Events in a Million New York Times Articles. ECML/PKDD (3) 2010: 615-618 - [c21]Kleanthis-Nikolaos Kontonasios, Tijl De Bie:
An Information-Theoretic Approach to Finding Informative Noisy Tiles in Binary Databases. SDM 2010: 153-164 - [c20]Omar Ali, Ilias N. Flaounas, Tijl De Bie, Nick Mosdell, Justin Lewis, Nello Cristianini:
Automating News Content Analysis: An Application to Gender Bias and Readability. WAPA 2010: 36-43 - [i3]Nick Fyson, Tijl De Bie, Nello Cristianini:
Reconstruction of Causal Networks by Set Covering. CoRR abs/1006.0849 (2010) - [i2]Tijl De Bie:
Maximum entropy models and subjective interestingness: an application to tiles in binary databases. CoRR abs/1008.3314 (2010)
2000 – 2009
- 2009
- [j5]Hong Sun, Tijl De Bie, Valerie Storms, Qiang Fu, Thomas Dhollander, Karen Lemmens, Annemieke Verstuyf, Bart De Moor, Kathleen Marchal:
ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules. BMC Bioinform. 10(S-1) (2009) - [c19]Marco Turchi, Tijl De Bie, Nello Cristianini:
Learning to translate: a statistical and computational analysis. SMART@EAMT 2009 - [c18]Tijl De Bie, Thiago Turchetti Maia, Antônio de Pádua Braga:
Machine Learning with Labeled and Unlabeled Data. ESANN 2009 - [c17]Marco Turchi, Tijl De Bie, Nello Cristianini:
An Intelligent Agent That Autonomously Learns How to Translate. IAT 2009: 12-19 - [c16]Ilias N. Flaounas, Marco Turchi, Tijl De Bie, Nello Cristianini:
Inference and Validation of Networks. ECML/PKDD (1) 2009: 344-358 - [c15]Marco Turchi, Ilias N. Flaounas, Omar Ali, Tijl De Bie, Tristan Snowsill, Nello Cristianini:
Found in Translation. ECML/PKDD (2) 2009: 746-749 - [i1]Tijl De Bie:
Explicit probabilistic models for databases and networks. CoRR abs/0906.5148 (2009) - 2008
- [c14]Anneleen Daemen, Olivier Gevaert, Tijl De Bie, Annelies Debucquoy, Jean-Pascal Machiels, Bart De Moor, Karin Haustermans:
Integrating Microarray and Proteomics Data to Predict the Response of Cetuximab in Patients with Rectal Cancer. Pacific Symposium on Biocomputing 2008: 166-177 - [c13]Marco Turchi, Tijl De Bie, Nello Cristianini:
Learning Performance of a Machine Translation System: a Statistical and Computational Analysis. WMT@ACL 2008: 35-43 - 2007
- [j4]Margherita Bresco, Marco Turchi, Tijl De Bie, Nello Cristianini:
Modeling sequence evolution with kernel methods. Comput. Optim. Appl. 38(2): 281-298 (2007) - [c12]Elisa Ricci, Tijl De Bie, Nello Cristianini:
Discriminative Sequence Labeling by Z-Score Optimization. ECML 2007: 274-285 - [c11]Tijl De Bie:
Deploying SDP for machine learning. ESANN 2007: 205-210 - [c10]Sándor Szedmák, Tijl De Bie, David R. Hardoon:
A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning. ESANN 2007: 211-216 - [c9]Elisa Ricci, Tijl De Bie, Nello Cristianini:
Learning to Align: A Statistical Approach. IDA 2007: 25-36 - [c8]