| LS-SVMlab toolbox user's guide: version 1.7 K De Brabanter, P Karsmakers, F Ojeda, C Alzate, J De Brabanter, ... Katholieke Universiteit Leuven, 2010 | 355 | 2010 |
| LS-SVMlab toolbox user's guide: version 1.7 K De Brabanter, P Karsmakers, F Ojeda, C Alzate, J De Brabanter, ... Katholieke Universiteit Leuven, 2010 | 355 | 2010 |
| Multiway spectral clustering with out-of-sample extensions through weighted kernel PCA C Alzate, JAK Suykens IEEE transactions on pattern analysis and machine intelligence 32 (2), 335-347, 2008 | 288 | 2008 |
| Show me your evidence-an automatic method for context dependent evidence detection R Rinott, L Dankin, C Alzate, MM Khapra, E Aharoni, N Slonim Proceedings of the 2015 conference on empirical methods in natural language …, 2015 | 282 | 2015 |
| An autonomous debating system N Slonim, Y Bilu, C Alzate, R Bar-Haim, B Bogin, F Bonin, L Choshen, ... Nature 591 (7850), 379-384, 2021 | 188 | 2021 |
| LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines R Langone, C Alzate, B De Ketelaere, J Vlasselaer, W Meert, ... Engineering Applications of Artificial Intelligence 37, 268-278, 2015 | 103 | 2015 |
| Improved electricity load forecasting via kernel spectral clustering of smart meters C Alzate, M Sinn 2013 IEEE 13th International Conference on Data Mining, 943-948, 2013 | 78 | 2013 |
| Will it blend? blending weak and strong labeled data in a neural network for argumentation mining E Shnarch, C Alzate, L Dankin, M Gleize, Y Hou, L Choshen, R Aharonov, ... Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 77 | 2018 |
| Corpus wide argument mining—a working solution L Ein-Dor, E Shnarch, L Dankin, A Halfon, B Sznajder, A Gera, C Alzate, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7683-7691, 2020 | 70 | 2020 |
| Kernel component analysis using an epsilon-insensitive robust loss function C Alzate, JAK Suykens IEEE Transactions on Neural Networks 19 (9), 1583-1598, 2008 | 65 | 2008 |
| Multiclass semisupervised learning based upon kernel spectral clustering S Mehrkanoon, C Alzate, R Mall, R Langone, JAK Suykens IEEE transactions on neural networks and learning systems 26 (4), 720-733, 2014 | 60 | 2014 |
| Primal and dual model representations in kernel-based learning JAK Suykens, C Alzate, K Pelckmans | 45 | 2010 |
| Noise level estimation for model selection in kernel PCA denoising C Varon, C Alzate, JAK Suykens IEEE transactions on neural networks and learning systems 26 (11), 2650-2663, 2015 | 44 | 2015 |
| Kernel spectral clustering and applications R Langone, R Mall, C Alzate, JAK Suykens Unsupervised learning algorithms, 135-161, 2016 | 40 | 2016 |
| A weighted kernel PCA formulation with out-of-sample extensions for spectral clustering methods C Alzate, JAK Suykens The 2006 IEEE International Joint Conference on Neural Network Proceedings …, 2006 | 40 | 2006 |
| Kernel spectral clustering with memory effect R Langone, C Alzate, JAK Suykens Physica A: Statistical Mechanics and its Applications 392 (10), 2588-2606, 2013 | 35 | 2013 |
| A regularized kernel CCA contrast function for ICA C Alzate, JAK Suykens Neural Networks 21 (2-3), 170-181, 2008 | 34 | 2008 |
| Sparse kernel spectral clustering models for large-scale data analysis C Alzate, JAK Suykens Neurocomputing 74 (9), 1382-1390, 2011 | 33 | 2011 |
| Hierarchical kernel spectral clustering C Alzate, JAK Suykens Neural Networks 35, 21-30, 2012 | 31 | 2012 |
| Image segmentation using a weighted kernel PCA approach to spectral clustering C Alzate, JAK Suykens 2007 IEEE Symposium on Computational Intelligence in Image and Signal …, 2007 | 31 | 2007 |
