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2020 – today
- 2024
[j15]Pieter Dewulf
, Michiel Stock, Bernard De Baets:
Link Prediction in Stagewise Graphs. IEEE Trans. Knowl. Data Eng. 36(7): 3252-3264 (2024)- [i10]Michiel Stock, Dimitri Boeckaerts, Pieter Dewulf, Steff Taelman, Maxime Van Haeverbeke, Wim Van Criekinge, Bernard De Baets:
Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data. CoRR abs/2402.17572 (2024) - 2023
- [j14]Maxime Van Haeverbeke, Bernard De Baets, Michiel Stock:
Evaluating the potential of Distribution of Relaxation Times analysis for plant agriculture. Comput. Electron. Agric. 213: 108249 (2023) - [i9]Pieter Dewulf, Michiel Stock, Bernard De Baets:
The Hyperdimensional Transform: a Holographic Representation of Functions. CoRR abs/2310.16065 (2023) - [i8]Pieter Dewulf, Bernard De Baets, Michiel Stock:
The Hyperdimensional Transform for Distributional Modelling, Regression and Classification. CoRR abs/2311.08150 (2023) - 2022
- [j13]Maxime Van Haeverbeke, Michiel Stock, Bernard De Baets:
Equivalent Electrical Circuits and Their Use Across Electrochemical Impedance Spectroscopy Application Domains. IEEE Access 10: 51363-51379 (2022) - [j12]Kirsten Van Huffel, Michiel Stock, Bernard De Baets:
BioCCP.jl: collecting coupons in combinatorial biotechnology. Bioinform. 38(4): 1144-1145 (2022) - 2021
- [j11]Michiel Stock, Bac Nguyen, Wouter Courtens, Hilbran Verstraete, Eric W. M. Stienen, Bernard De Baets:
Otolith identification using a deep hierarchical classification model. Comput. Electron. Agric. 180: 105883 (2021) - [j10]Michiel Stock, Laura Hoebeke, Bernard De Baets:
Disentangling the Information in Species Interaction Networks. Entropy 23(6): 703 (2021) - [j9]Maxime Van Haeverbeke, Michiel Stock, Bernard De Baets:
Practical Equivalent Electrical Circuit Identification for Electrochemical Impedance Spectroscopy Analysis With Gene Expression Programming. IEEE Trans. Instrum. Meas. 70: 1-12 (2021) - 2020
- [j8]Michiel Stock, Tapio Pahikkala, Antti Airola, Willem Waegeman, Bernard De Baets:
Algebraic shortcuts for leave-one-out cross-validation in supervised network inference. Briefings Bioinform. 21(1): 262-271 (2020) - [j7]Olivier Pieters, Tom De Swaef, Peter Lootens, Michiel Stock, Isabel Roldán-Ruiz, Francis Wyffels:
Limitations of snapshot hyperspectral cameras to monitor plant response dynamics in stress-free conditions. Comput. Electron. Agric. 179: 105825 (2020) - [j6]Olivier Pieters, Tom De Swaef, Peter Lootens, Michiel Stock, Isabel Roldán-Ruiz, Francis Wyffels:
Gloxinia - An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants. Sensors 20(11): 3055 (2020)
2010 – 2019
- 2018
- [j5]Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression. Neural Comput. 30(8) (2018) - [i7]Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression. CoRR abs/1803.01575 (2018) - 2016
- [j4]Michiel Stock, Krzysztof Dembczynski, Bernard De Baets, Willem Waegeman:
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models. Data Min. Knowl. Discov. 30(5): 1370-1394 (2016) - [i6]Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method. CoRR abs/1606.04275 (2016) - [i5]Michiel Stock, Krzysztof Dembczynski, Bernard De Baets, Willem Waegeman:
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models. CoRR abs/1606.04278 (2016) - 2014
- [j3]Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Identification of Functionally Related Enzymes by Learning-to-Rank Methods. IEEE ACM Trans. Comput. Biol. Bioinform. 11(6): 1157-1169 (2014) - [c1]Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman:
A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction. ECML/PKDD (2) 2014: 517-532 - [i4]Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Identification of functionally related enzymes by learning-to-rank methods. CoRR abs/1405.4394 (2014) - [i3]Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman:
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction. CoRR abs/1405.4423 (2014) - 2013
- [j2]Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman:
Efficient regularized least-squares algorithms for conditional ranking on relational data. Mach. Learn. 93(2-3): 321-356 (2013) - 2012
- [j1]Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets:
A Kernel-Based Framework for Learning Graded Relations From Data. IEEE Trans. Fuzzy Syst. 20(6): 1090-1101 (2012) - [i2]Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman:
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data. CoRR abs/1209.4825 (2012) - 2011
- [i1]Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets:
A kernel-based framework for learning graded relations from data. CoRR abs/1111.6473 (2011)
Coauthor Index
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